162 jobs found

Sr. Distinguished AI Engineer (Agentic AI Platform) Overview: At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. Our investments in technology infrastructure and world-class talent - along with our deep experience in machine learning - position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. Team Description: The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We work hand-in-hand with our partners across the company to advance the state of the art in science and AI engineering, and we build and deploy proprietary solutions that are central to our business and deliver value to millions of customers. Our AI models and platforms empower teams across Capital One to enhance their products with the transformative power of AI, in responsible and scalable ways for the highest leverage impact. In this role, you will: Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One. You will contribute to the north star platform architecture, continuously publishing and refining living diagrams and canonical APIs that cover agent orchestration, RAG pipelines, prompt libraries and multi-tenant policy enforcement. A major emphasis is around standardizing and automating agentic workflows : you will evaluate agentic frameworks such LangGraph, AutoGen, Semantic Kernal, CrewAI and LlamaIndex and then harden / blend patterns that best meet enterprise SLAs do that 90% of new apps adopt them. Developer experience is another cornerstone. You will contribute to crafting an end to end GenAI SDK, CLI and starter kits that let AI engineers spin up secure, observable agentic workflows in under minutes, shrinking prototyping to production timelines by 30%. Trust and safety remain paramount; you will help bring together a vision of central guardrail services - prompt firewalls, content-filter hooks, red team harnesses and audit APIs - consumed by every application to ensure zero Sev4 incidents. You will collaborate with cross organization architects to drive end to end performance by optimizing orchestration - level batching, retrieval caching, heuristic tuning to achieve reductions in per token spend. You will accelerate innovation by incubating proof of concepts and driving RFCs such as hierarchical agent memory, multimodal guardrails, multimodal RAG. You'll own central Helm charts, operators and CRDs that auto scale agents to hit tenant SLAs Finally you will coach and evangelize - hosting architecture office hours, mentoring Staff, Principal and Senior engineers, authoring technical design documents and blogs and representing Capital One at Tier1 AI conferences - to amplify platform vision across internal and external communities. The Ideal Candidate: You love to build systems, take pride in the quality of your work, and also share our passion to do the right thing. You want to work on problems that will help change banking for good. Passion for staying abreast of the latest research, and an ability to intuitively understand scientific publications and judiciously apply novel techniques in production. You adapt quickly and thrive on bringing clarity to big, undefined problems. You love asking questions and digging deep to uncover the root of problems and can articulate your findings concisely with clarity. You have the courage to share new ideas even when they are unproven. You are deeply Technical. You possess a strong foundation in engineering and mathematics, and your expertise in hardware, software, and AI enable you to see and exploit optimization opportunities that others miss. You are a resilient trail blazer who can forge new paths to achieve business goals when the route is unknown. Basic Qualifications: Bachelor's degree in Computer Science, Engineering, or AI plus at least 10 years of experience developing AI and ML algorithms or technologies, or Master's degree plus at least 8 years of experience developing AI and ML algorithms or technologies At least 10 years of experience programming with Python, Go, Scala, or Java Preferred Qualifications: 9 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud) 2+ years of experience supporting Agentic Frameworks (LangChain, CrewAI, Semantic Kernel (Microsoft), or AutoGen) 2+ years of experience with LLMOps (Google Cloud Vertex AI, Amazon SageMaker, Azure Machine Learning) 8+ years of experience designing mission-critical machine learning platforms 2+ years of experience architecting, designing, developing, integrating, delivering, and supporting complex AI systems Demonstrated ability to lead and mentor multiple engineering teams and influence cross-functional stakeholders up to the VP level Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, or Golang Master's degree in Computer Science, Computer Engineering, or relevant technical field Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers Experience leading GenAI or LLM-Powered application architectures in production Deep understanding of Responsible AI, data privacy and multi-tenant security patterns K8s mastery (multi-region clusters, service mesh) Experience staying abreast of the latest AI research and AI systems and applying novel techniques in production Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $314,800 - $359,300 for Sr. Distinguished AI Engineer Cambridge, MA: $314,800 - $359,300 for Sr. Distinguished AI Engineer New York, NY: $343,400 - $392,000 for Sr. Distinguished AI Engineer San Francisco, CA: $343,400 - $392,000 for Sr. Distinguished AI Engineer San Jose, CA: $343,400 - $392,000 for Sr. Distinguished AI Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . click apply for full job details

Sr. Distinguished AI Engineer (Agentic AI Platform) Overview: At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. Our investments in technology infrastructure and world-class talent - along with our deep experience in machine learning - position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. Team Description: The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We work hand-in-hand with our partners across the company to advance the state of the art in science and AI engineering, and we build and deploy proprietary solutions that are central to our business and deliver value to millions of customers. Our AI models and platforms empower teams across Capital One to enhance their products with the transformative power of AI, in responsible and scalable ways for the highest leverage impact. In this role, you will: Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One. You will contribute to the north star platform architecture, continuously publishing and refining living diagrams and canonical APIs that cover agent orchestration, RAG pipelines, prompt libraries and multi-tenant policy enforcement. A major emphasis is around standardizing and automating agentic workflows : you will evaluate agentic frameworks such LangGraph, AutoGen, Semantic Kernal, CrewAI and LlamaIndex and then harden / blend patterns that best meet enterprise SLAs do that 90% of new apps adopt them. Developer experience is another cornerstone. You will contribute to crafting an end to end GenAI SDK, CLI and starter kits that let AI engineers spin up secure, observable agentic workflows in under minutes, shrinking prototyping to production timelines by 30%. Trust and safety remain paramount; you will help bring together a vision of central guardrail services - prompt firewalls, content-filter hooks, red team harnesses and audit APIs - consumed by every application to ensure zero Sev4 incidents. You will collaborate with cross organization architects to drive end to end performance by optimizing orchestration - level batching, retrieval caching, heuristic tuning to achieve reductions in per token spend. You will accelerate innovation by incubating proof of concepts and driving RFCs such as hierarchical agent memory, multimodal guardrails, multimodal RAG. You'll own central Helm charts, operators and CRDs that auto scale agents to hit tenant SLAs Finally you will coach and evangelize - hosting architecture office hours, mentoring Staff, Principal and Senior engineers, authoring technical design documents and blogs and representing Capital One at Tier1 AI conferences - to amplify platform vision across internal and external communities. The Ideal Candidate: You love to build systems, take pride in the quality of your work, and also share our passion to do the right thing. You want to work on problems that will help change banking for good. Passion for staying abreast of the latest research, and an ability to intuitively understand scientific publications and judiciously apply novel techniques in production. You adapt quickly and thrive on bringing clarity to big, undefined problems. You love asking questions and digging deep to uncover the root of problems and can articulate your findings concisely with clarity. You have the courage to share new ideas even when they are unproven. You are deeply Technical. You possess a strong foundation in engineering and mathematics, and your expertise in hardware, software, and AI enable you to see and exploit optimization opportunities that others miss. You are a resilient trail blazer who can forge new paths to achieve business goals when the route is unknown. Basic Qualifications: Bachelor's degree in Computer Science, Engineering, or AI plus at least 10 years of experience developing AI and ML algorithms or technologies, or Master's degree plus at least 8 years of experience developing AI and ML algorithms or technologies At least 10 years of experience programming with Python, Go, Scala, or Java Preferred Qualifications: 9 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud) 2+ years of experience supporting Agentic Frameworks (LangChain, CrewAI, Semantic Kernel (Microsoft), or AutoGen) 2+ years of experience with LLMOps (Google Cloud Vertex AI, Amazon SageMaker, Azure Machine Learning) 8+ years of experience designing mission-critical machine learning platforms 2+ years of experience architecting, designing, developing, integrating, delivering, and supporting complex AI systems Demonstrated ability to lead and mentor multiple engineering teams and influence cross-functional stakeholders up to the VP level Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, or Golang Master's degree in Computer Science, Computer Engineering, or relevant technical field Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers Experience leading GenAI or LLM-Powered application architectures in production Deep understanding of Responsible AI, data privacy and multi-tenant security patterns K8s mastery (multi-region clusters, service mesh) Experience staying abreast of the latest AI research and AI systems and applying novel techniques in production Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $314,800 - $359,300 for Sr. Distinguished AI Engineer Cambridge, MA: $314,800 - $359,300 for Sr. Distinguished AI Engineer New York, NY: $343,400 - $392,000 for Sr. Distinguished AI Engineer San Francisco, CA: $343,400 - $392,000 for Sr. Distinguished AI Engineer San Jose, CA: $343,400 - $392,000 for Sr. Distinguished AI Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . click apply for full job details

Distinguished AI Engineer (Agentic AI Platform) At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. Our investments in technology infrastructure and world-class talent - along with our deep experience in machine learning - position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. Team Description: The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We work hand-in-hand with our partners across the company to advance the state of the art in science and AI engineering, and we build and deploy proprietary solutions that are central to our business and deliver value to millions of customers. Our AI models and platforms empower teams across Capital One to enhance their products with the transformative power of AI, in responsible and scalable ways for the highest leverage impact. Why this Role Matters: We are building an enterprise Generative AI Platform that lets dozens of product teams compose powerful, safe and explainable AI capabilities - without wrestling with model minutiae or infra plumbing. You will design the agentic workflow framework, shared services such as memory, guardrails, vector search, SDKs and blueprints that translate foundation model power into production grade applications used by millions of users across multiple lines of businesses. In this role, you will: Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One. You will contribute to the north star platform architecture, continuously publishing and refining living diagrams and canonical APIs that cover agent orchestration, RAG pipelines, prompt libraries and multi-tenant policy enforcement. A major emphasis is around standardizing and automating agentic workflows : you will evaluate agentic frameworks such LangGraph, AutoGen, Semantic Kernal, CrewAI and LlamaIndex and then harden / blend patterns that best meet enterprise SLAs do that 90% of new apps adopt them. Developer experience is another cornerstone. You will contribute to crafting an end to end GenAI SDK, CLI and starter kits that let AI engineers spin up secure, observable agentic workflows in under minutes, shrinking prototyping to production timelines by 30%. Trust and safety remain paramount; you will help bring together a vision of central guardrail services - prompt firewalls, content-filter hooks, red team harnesses and audit APIs - consumed by every application to ensure zero Sev4 incidents. You will collaborate with cross organization architects to drive end to end performance by optimizing orchestration - level batching, retrieval caching, heuristic tuning to achieve reductions in per token spend. You will accelerate innovation by incubating proof of concepts and driving RFCs such as hierarchical agent memory, multimodal guardrails, multimodal RAG. You'll own central Helm charts, operators and CRDs that auto scale agents to hit tenant SLAs Finally you will coach and evangelize - hosting architecture office hours, mentoring Staff, Principal and Senior engineers, authoring technical design documents and blogs and representing Capital One at Tier1 AI conferences - to amplify platform vision across internal and external communities. Basic Qualifications: Bachelor's degree in Computer Science, Engineering, or AI plus at least 8 years of experience developing AI and ML algorithms or technologies, or Master's degree plus at least 6 years of experience developing AI and ML algorithms or technologies At least 8 years of experience programming with Python, Go, Scala, or Java Preferred Qualifications: 8+ years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud) 2+ years of experience supporting Agentic Frameworks (LangChain, CrewAI, Semantic Kernel (Microsoft), or AutoGen) 2+ years of experience with LLMOps (Google Cloud Vertex AI, Amazon SageMaker, Azure Machine Learning) 8+ years of experience designing mission-critical machine learning platforms 2+ years of experience architecting, designing, developing, integrating, delivering, and supporting complex AI systems Demonstrated ability to lead and mentor multiple engineering teams and influence cross-functional stakeholders up to the VP level Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, or Golang Master's degree in Computer Science, Computer Engineering, or relevant technical field Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers Experience leading GenAI or LLM-Powered application architectures in production Deep understanding of Responsible AI, data privacy and multi-tenant security patterns Experience as a Staff-plus or Distinguished IC engineer influencing 50+ engineers and C-suite stakeholders K8s mastery (multi-region clusters, sericie mesh) Experience staying abreast of the latest AI research and AI systems and applying novel techniques in production Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $269,100 - $307,200 for Distinguished AI Engineer McLean, VA: $269,100 - $307,200 for Distinguished AI Engineer New York, NY: $293,600 - $335,100 for Distinguished AI Engineer San Francisco, CA: $293,600 - $335,100 for Distinguished AI Engineer San Jose, CA: $293,600 - $335,100 for Distinguished AI Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada . click apply for full job details

Distinguished AI Engineer (Agentic AI Platform) At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. Our investments in technology infrastructure and world-class talent - along with our deep experience in machine learning - position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. Team Description: The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We work hand-in-hand with our partners across the company to advance the state of the art in science and AI engineering, and we build and deploy proprietary solutions that are central to our business and deliver value to millions of customers. Our AI models and platforms empower teams across Capital One to enhance their products with the transformative power of AI, in responsible and scalable ways for the highest leverage impact. Why this Role Matters: We are building an enterprise Generative AI Platform that lets dozens of product teams compose powerful, safe and explainable AI capabilities - without wrestling with model minutiae or infra plumbing. You will design the agentic workflow framework, shared services such as memory, guardrails, vector search, SDKs and blueprints that translate foundation model power into production grade applications used by millions of users across multiple lines of businesses. In this role, you will: Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One. You will contribute to the north star platform architecture, continuously publishing and refining living diagrams and canonical APIs that cover agent orchestration, RAG pipelines, prompt libraries and multi-tenant policy enforcement. A major emphasis is around standardizing and automating agentic workflows : you will evaluate agentic frameworks such LangGraph, AutoGen, Semantic Kernal, CrewAI and LlamaIndex and then harden / blend patterns that best meet enterprise SLAs do that 90% of new apps adopt them. Developer experience is another cornerstone. You will contribute to crafting an end to end GenAI SDK, CLI and starter kits that let AI engineers spin up secure, observable agentic workflows in under minutes, shrinking prototyping to production timelines by 30%. Trust and safety remain paramount; you will help bring together a vision of central guardrail services - prompt firewalls, content-filter hooks, red team harnesses and audit APIs - consumed by every application to ensure zero Sev4 incidents. You will collaborate with cross organization architects to drive end to end performance by optimizing orchestration - level batching, retrieval caching, heuristic tuning to achieve reductions in per token spend. You will accelerate innovation by incubating proof of concepts and driving RFCs such as hierarchical agent memory, multimodal guardrails, multimodal RAG. You'll own central Helm charts, operators and CRDs that auto scale agents to hit tenant SLAs Finally you will coach and evangelize - hosting architecture office hours, mentoring Staff, Principal and Senior engineers, authoring technical design documents and blogs and representing Capital One at Tier1 AI conferences - to amplify platform vision across internal and external communities. Basic Qualifications: Bachelor's degree in Computer Science, Engineering, or AI plus at least 8 years of experience developing AI and ML algorithms or technologies, or Master's degree plus at least 6 years of experience developing AI and ML algorithms or technologies At least 8 years of experience programming with Python, Go, Scala, or Java Preferred Qualifications: 8+ years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud) 2+ years of experience supporting Agentic Frameworks (LangChain, CrewAI, Semantic Kernel (Microsoft), or AutoGen) 2+ years of experience with LLMOps (Google Cloud Vertex AI, Amazon SageMaker, Azure Machine Learning) 8+ years of experience designing mission-critical machine learning platforms 2+ years of experience architecting, designing, developing, integrating, delivering, and supporting complex AI systems Demonstrated ability to lead and mentor multiple engineering teams and influence cross-functional stakeholders up to the VP level Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, or Golang Master's degree in Computer Science, Computer Engineering, or relevant technical field Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers Experience leading GenAI or LLM-Powered application architectures in production Deep understanding of Responsible AI, data privacy and multi-tenant security patterns Experience as a Staff-plus or Distinguished IC engineer influencing 50+ engineers and C-suite stakeholders K8s mastery (multi-region clusters, sericie mesh) Experience staying abreast of the latest AI research and AI systems and applying novel techniques in production Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $269,100 - $307,200 for Distinguished AI Engineer McLean, VA: $269,100 - $307,200 for Distinguished AI Engineer New York, NY: $293,600 - $335,100 for Distinguished AI Engineer San Francisco, CA: $293,600 - $335,100 for Distinguished AI Engineer San Jose, CA: $293,600 - $335,100 for Distinguished AI Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada . click apply for full job details

Job DescriptionJob DescriptionCompany Description CapTech is an award-winning consulting firm that collaborates with clients to achieve what's possible through the power of technology. At CapTech, we're passionate about the work we do and the results we achieve for our clients. From the outset, our founders shared a collective passion to create a consultancy centered on strong relationships that would stand the test of time. Today we work alongside clients that include Fortune 100 companies, mid-sized enterprises, and government agencies, a list that spans across the country. Job Description CapTech Data Engineering consultants enable clients to build and maintain advanced data systems that bring together data from disparate sources in order to enable decision-makers. We build pipelines and prepare data for use by data scientists, data analysts, and other data systems. We love solving problems and providing creative solutions for our clients. Cloud Data Engineers leverage the client's cloud infrastructure to deliver this value today and to scale for the future. We enjoy a collaborative environment and have many opportunities to learn from and share knowledge with other developers, architects, and our clients. Specific responsibilities for the Data Engineer - Cloud position include: Developing data pipelines and other data products using Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP) Advising clients on specific technologies and methodologies for utilizing cloud resources to efficiently ingest and process data quickly Utilizing your skills in engineering best practices to solve complex data problems Collaborating with end users, development staff, and business analysts to ensure that prospective data architecture plans maximize the value of client data across the organization. Articulating architectural differences between solution methods and the advantages/disadvantages of each Qualifications Typical experience for successful candidates includes: Experience delivering solutions on a major cloud platform Ability to think strategically and relate architectural decisions/recommendations to business needs and client culture Experience in the design and implementation of data architecture solutions A wide range of production database experience, usually including substantial SQL expertise, database administration, and scripting data pipelines Ability to assess and utilize traditional and modern architectural components required based on business needs. A demonstrable ability to deliver production data pipelines and other data products. This could be hands on experience, degree, certification, bootcamp, or other learning. Skills: Successful candidates usually have demonstrable experience with technologies in some of these categories: Languages: SQL, Python, Java, R, C# / C++ / C Database: SQL Server, PostgreSQL, Snowflake, Redshift, Aurora, Presto, BigQuery, Oracle DevOps: git, docker, subversion, Kubernetes, Jenkins Additional Technologies: Spark, Databricks, Kafka, Kinesis, Hadoop, Lambda, EMR Popular Certifications: AWS Cloud Practitioner, Microsoft Azure Data Fundamentals, Google Associate Cloud Engineer Additional Information We want everyone at CapTech to be able to envision a lasting and rewarding career here, which is why we offer a variety of career paths based on your skills and passions. You decide where and how you want to develop, and we help get you there with customizable career progression and a comprehensive benefits package to support you along the way. Alongside our suite of traditional benefits encompassing generous PTO, health coverage, disability insurance, paid family leave and more, we've launched extended benefits to help meet our employees' needs. CapTech is committed to providing a flexible work environment and helping our employees achieve a work-life balance that suits their individual needs. Employees must be available to work onsite in a client location or a CapTech office as requested. We allow CapTech employees to work remotely when compatible with CapTech and client needs. Learning & Development - Programs offering certification and tuition support, digital on-demand learning courses, mentorship, and skill development paths Modern Health -A mental health and well-being platform that provides 1:1 care, group support sessions, and self-serve resources to support employees and their families through life's ups and downs Carrot Fertility -Inclusive fertility and family-forming coverage for all paths to parenthood - including adoption, surrogacy, fertility treatments, pregnancy, and more - and opportunities for employer-sponsored funds to help pay for care Fringe -A company paid stipend program for personalized lifestyle benefits, allowing employees to choose benefits that matter most to them - ranging from vendors like Netflix, Spotify, and GrubHub to services like student loan repayment, travel, fitness, and more Employee Resource Groups - Employee-led committees that embrace and incorporate diversity and inclusion into our day-to-day operations Philanthropic Partnerships - Opportunities to engage in partnerships and pro-bono projects that support our communities. 401(k) Matching - Generous matching and no vesting period to help you continue to build financial wellness CapTech is an equal opportunity employer committed to fostering a culture of equality, inclusion and fairness - each foundational to our core values. We strive to create a diverse environment where each employee is encouraged to bring their unique ideas, backgrounds and experiences to the workplace. For more information about our Diversity, Inclusion and Belonging efforts, click HERE. As part of this commitment, CapTech will ensure that persons with disabilities are provided reasonable accommodations. If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact Laura Massa directly via email . At this time, CapTech cannot transfer nor sponsor a work visa for this position. Applicants must be authorized to work directly for any employer in the United States without visa sponsorship.

Job DescriptionJob DescriptionCompany Description CapTech is an award-winning consulting firm that collaborates with clients to achieve what's possible through the power of technology. At CapTech, we're passionate about the work we do and the results we achieve for our clients. From the outset, our founders shared a collective passion to create a consultancy centered on strong relationships that would stand the test of time. Today we work alongside clients that include Fortune 100 companies, mid-sized enterprises, and government agencies, a list that spans across the country. Job Description CapTech Data Engineering consultants enable clients to build and maintain advanced data systems that bring together data from disparate sources in order to enable decision-makers. We build pipelines and prepare data for use by data scientists, data analysts, and other data systems. We love solving problems and providing creative solutions for our clients. Cloud Data Engineers leverage the client's cloud infrastructure to deliver this value today and to scale for the future. We enjoy a collaborative environment and have many opportunities to learn from and share knowledge with other developers, architects, and our clients. Specific responsibilities for the Data Engineer - Cloud position include: Developing data pipelines and other data products using Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP) Advising clients on specific technologies and methodologies for utilizing cloud resources to efficiently ingest and process data quickly Utilizing your skills in engineering best practices to solve complex data problems Collaborating with end users, development staff, and business analysts to ensure that prospective data architecture plans maximize the value of client data across the organization. Articulating architectural differences between solution methods and the advantages/disadvantages of each Qualifications Typical experience for successful candidates includes: Experience delivering solutions on a major cloud platform Ability to think strategically and relate architectural decisions/recommendations to business needs and client culture Experience in the design and implementation of data architecture solutions A wide range of production database experience, usually including substantial SQL expertise, database administration, and scripting data pipelines Ability to assess and utilize traditional and modern architectural components required based on business needs. A demonstrable ability to deliver production data pipelines and other data products. This could be hands on experience, degree, certification, bootcamp, or other learning. Skills: Successful candidates usually have demonstrable experience with technologies in some of these categories: Languages: SQL, Python, Java, R, C# / C++ / C Database: SQL Server, PostgreSQL, Snowflake, Redshift, Aurora, Presto, BigQuery, Oracle DevOps: git, docker, subversion, Kubernetes, Jenkins Additional Technologies: Spark, Databricks, Kafka, Kinesis, Hadoop, Lambda, EMR Popular Certifications: AWS Cloud Practitioner, Microsoft Azure Data Fundamentals, Google Associate Cloud Engineer Additional Information We want everyone at CapTech to be able to envision a lasting and rewarding career here, which is why we offer a variety of career paths based on your skills and passions. You decide where and how you want to develop, and we help get you there with customizable career progression and a comprehensive benefits package to support you along the way. Alongside our suite of traditional benefits encompassing generous PTO, health coverage, disability insurance, paid family leave and more, we've launched extended benefits to help meet our employees' needs. CapTech is committed to providing a flexible work environment and helping our employees achieve a work-life balance that suits their individual needs. Employees must be available to work onsite in a client location or a CapTech office as requested. We allow CapTech employees to work remotely when compatible with CapTech and client needs. Learning & Development - Programs offering certification and tuition support, digital on-demand learning courses, mentorship, and skill development paths Modern Health -A mental health and well-being platform that provides 1:1 care, group support sessions, and self-serve resources to support employees and their families through life's ups and downs Carrot Fertility -Inclusive fertility and family-forming coverage for all paths to parenthood - including adoption, surrogacy, fertility treatments, pregnancy, and more - and opportunities for employer-sponsored funds to help pay for care Fringe -A company paid stipend program for personalized lifestyle benefits, allowing employees to choose benefits that matter most to them - ranging from vendors like Netflix, Spotify, and GrubHub to services like student loan repayment, travel, fitness, and more Employee Resource Groups - Employee-led committees that embrace and incorporate diversity and inclusion into our day-to-day operations Philanthropic Partnerships - Opportunities to engage in partnerships and pro-bono projects that support our communities. 401(k) Matching - Generous matching and no vesting period to help you continue to build financial wellness CapTech is an equal opportunity employer committed to fostering a culture of equality, inclusion and fairness - each foundational to our core values. We strive to create a diverse environment where each employee is encouraged to bring their unique ideas, backgrounds and experiences to the workplace. For more information about our Diversity, Inclusion and Belonging efforts, click HERE. As part of this commitment, CapTech will ensure that persons with disabilities are provided reasonable accommodations. If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact Laura Massa directly via email . At this time, CapTech cannot transfer nor sponsor a work visa for this position. Applicants must be authorized to work directly for any employer in the United States without visa sponsorship.

Job DescriptionJob Description Crusoe is on a mission to accelerate the abundance of energy and intelligence. As the only vertically integrated AI infrastructure company built from the ground up, we own and operate each layer of the stack - from electrons to tokens - to power the world's most ambitious AI workloads. When you join Crusoe, you join a team that is building the future, faster. We're in the midst of the greatest industrial revolution of our time. The demand for AI compute is boundless, and power is a bottleneck. We're solving that - with an energy-first approach that makes AI infrastructure better for the world and faster for the people innovating with AI. We're looking for problem-solving, opportunity-finding teammates with a sense of urgency, who believe in the scale of our ambition and thrive on a path not fully paved - people who want to grow their careers alongside a team of experts across energy, manufacturing, data center construction, and cloud services. If you want to do the most meaningful work of your career, help our customers and partners advance their AI strategies, and be part of a high-performing team that believes in each other, come build with us at Crusoe. About This Role We're seeking a Staff Enterprise AI Automation Engineer to play a key role in executing Crusoe's 2026 Enterprise AI Strategy. In this role, you will design and build agentic AI systems that move the organization from simple information retrieval to orchestrated, multi-system automation. You'll operate at the intersection of AI, enterprise systems, and integration platforms-building scalable agent workflows, enabling a citizen developer ecosystem, and establishing the technical foundations for an AI-powered operating model. What You'll Be Working On Designing and implementing agentic AI workflows using a modular, API-first architecture across platforms such as Workato ONE, Anthropic Claude, and Gemini. Building autonomous agents that orchestrate workflows across enterprise systems (e.g., Salesforce, Coupa, Slack, Google Workspace) Architecting and integrating a unified data layer that enables AI agents to access and act on data across siloed systems Developing integrations, APIs, and custom connectors that enable scalable AI orchestration across business platforms Implementing MCP (Model Context Protocol) connectors and model-agnostic orchestration patterns Designing deployment pipelines and lifecycle management systems for AI agents in production environments Embedding security, data privacy, and compliance guardrails into all AI implementations Creating reusable templates, frameworks, and tooling to support a Citizen Developer Program Mentoring internal teams through code reviews, training, and technical enablement programs Evaluating emerging AI technologies and prototyping next-generation capabilities to advance agentic maturity What You'll Bring to the Team 10+ years of software engineering experience, including 3+ years in AI/ML or AI application development Strong proficiency in Python and API development (REST, GraphQL, webhooks) Hands-on experience with enterprise integration platforms (e.g., Workato, MuleSoft, Zapier) Experience working with LLM APIs (OpenAI, Anthropic, Google Gemini, or similar) Deep understanding of agentic architectures, RAG patterns, and prompt engineering Experience designing scalable, distributed systems in cloud environments (AWS, GCP, or Azure) Strong knowledge of microservices, event-driven architecture, and integration design patterns Experience with CI/CD, infrastructure as code, and DevOps practices Understanding of data security, privacy, and compliance considerations (SOC 2, GDPR) Bonus Points Experience deploying agentic AI systems in production environments Familiarity with iPaaS platforms (Workato preferred) and enterprise automation ecosystems Experience with Google Workspace or Microsoft 365 automation and extensibility Knowledge of Model Context Protocol (MCP) or similar interoperability standards Experience implementing AI governance frameworks in enterprise settings Background in infrastructure, energy, or high-performance computing environments Contributions to open-source AI projects or technical thought leadership Benefits: Competitive compensation Restricted Stock Units Paid time off & paid holidays Comprehensive health, dental & vision insurance Employer contributions to HSA account Paid parental leave Paid life insurance, short-term and long-term disability Professional development & tuition reimbursement Mental health & wellness support Commuter benefits (parking & transit) Cell phone stipend 401(k) Retirement plan with company match up to 4% of salary Volunteer time off Compensation Range Compensation will be paid in the range of up to $190,000 - $230,000 + Bonus. Restricted Stock Units are included in all offers. Compensation to be determined by the applicants knowledge, education, and abilities, as well as internal equity and alignment with market data. Crusoe is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, disability, genetic information, pregnancy, citizenship, marital status, sex/gender, sexual preference/ orientation, gender identity, age, veteran status, national origin, or any other status protected by law or regulation.

Job DescriptionJob DescriptionThe Role We are seeking a Staff Machine Learning Engineer with deep expertise in agentic AI - and a true passion for experimentation and creation - to design, build, test, evaluate, and productionize next-generation autonomous AI agents for healthcare and clinical research. If you love rapidly prototyping wild ideas, running build-test-learn cycles, iterating on novel agent behaviors, and turning unsolved challenges into working systems, this is the role for you. You will own end-to-end agentic workflows that reason, plan, use tools, orchestrate multi-agent collaboration, and deliver safe, reliable outcomes in highly regulated environments, while collaborating with multidisciplinary teams to influence Medeloop's technological direction. You will also be nested within a team of advisors and collaborators with deep medical and health expertise, including scientists, clinicians, and AI experts, including the former FDA commissioner, former editor of JAMA, and developer of BloombergGPT. The result: You will be an active participant in fostering a data lead public health and healthcare ecosystem. What You'll Own Lead the design and architecture of advanced agentic AI systems, including reasoning loops (ReAct, CoT, ToT), tool-calling, dynamic multi-agent orchestration, RAG pipelines, memory/state management, and emerging protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A). Build and own production-grade agent infrastructure, including prompts, function tools, workflow graphs, MCP/A2A integrations, and adaptive agent lifecycle management (spinning up, specializing, delegating, and decommissioning agents dynamically for complex healthcare workflows). Develop rigorous evaluation and safety frameworks - automated testing, benchmarking, regression testing, adversarial testing, safety guardrails, observability (tracing, logging, metrics), and human-in-the-loop mechanisms to ensure reliable, compliant performance in production. Drive LLM and ML model development - train, fine-tune, and deploy large-scale models on healthcare datasets, working closely with researchers and clinicians to solve real clinical challenges. Shape Medeloop's agentic AI strategy and roadmap in close partnership with the C-suite and cross-functional leadership. Stay at the cutting edge of agentic AI (multi-modal agents, advanced reasoning models, interoperability protocols) and help establish Medeloop as a leader in transparent, compliant healthcare AI. What We're Looking For 7+ years of hands-on experience as a Machine Learning Engineer, with a proven track record building and shipping production agentic AI systems (single- or multi-agent) in industry, ideally in healthcare, life sciences, or other related domains. Experience working on analytic engines (or advanced analytics platforms) - designing, optimizing, or integrating systems that power data-driven insights, queries, or decision-making at scale. Strong theoretical foundation in ML/AI, with emphasis on NLP/LLMs, reinforcement learning, planning/reasoning algorithms. Deep expertise with agentic frameworks and tools: LangChain/LangGraph, Model Context Protocol (MCP), Agent-to-Agent (A2A) protocols, Hugging Face, PyTorch, vector databases/semantic search, prompt engineering, and observability platforms (e.g., LangSmith, Phoenix). Experience designing fully automated evaluation and testing pipelines for autonomous agents and their orchestration, including metrics for reliability, safety, factuality, cost/latency, clinical utility, and dynamic behaviors. A builder/experimenter mindset - you thrive on rapid prototyping, testing bold new ideas, iterating quickly on agent designs, and exploring uncharted territory in agentic systems. Passion for unsolved challenges in healthcare AI, with the ability to thrive in a fast-paced, multidisciplinary environment and wear multiple hats. Bonus Points Strong record in top AI/ML conferences/journals; experience with healthcare data (EHRs, claims) and regulatory considerations (HIPAA, transparency, reproducibility). Multi-cloud experience (AWS, Azure, GCP) Why Medeloop Ownership from day one: small team, high-trust, no layers between your work and its impact Technically ambitious: you'll build AI-powered workflows, not just support them Real-world stakes: your work accelerates drug development, addresses health equity, and improves clinical research for institutions that matter Strong foundation: Series A, top-tier investors, and a data asset (200M+ patient records) that most companies spend years trying to build

Job DescriptionJob DescriptionThe Role We are seeking a Staff Machine Learning Engineer with deep expertise in agentic AI - and a true passion for experimentation and creation - to design, build, test, evaluate, and productionize next-generation autonomous AI agents for healthcare and clinical research. If you love rapidly prototyping wild ideas, running build-test-learn cycles, iterating on novel agent behaviors, and turning unsolved challenges into working systems, this is the role for you. You will own end-to-end agentic workflows that reason, plan, use tools, orchestrate multi-agent collaboration, and deliver safe, reliable outcomes in highly regulated environments, while collaborating with multidisciplinary teams to influence Medeloop's technological direction. You will also be nested within a team of advisors and collaborators with deep medical and health expertise, including scientists, clinicians, and AI experts, including the former FDA commissioner, former editor of JAMA, and developer of BloombergGPT. The result: You will be an active participant in fostering a data lead public health and healthcare ecosystem. What You'll Own Lead the design and architecture of advanced agentic AI systems, including reasoning loops (ReAct, CoT, ToT), tool-calling, dynamic multi-agent orchestration, RAG pipelines, memory/state management, and emerging protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A). Build and own production-grade agent infrastructure, including prompts, function tools, workflow graphs, MCP/A2A integrations, and adaptive agent lifecycle management (spinning up, specializing, delegating, and decommissioning agents dynamically for complex healthcare workflows). Develop rigorous evaluation and safety frameworks - automated testing, benchmarking, regression testing, adversarial testing, safety guardrails, observability (tracing, logging, metrics), and human-in-the-loop mechanisms to ensure reliable, compliant performance in production. Drive LLM and ML model development - train, fine-tune, and deploy large-scale models on healthcare datasets, working closely with researchers and clinicians to solve real clinical challenges. Shape Medeloop's agentic AI strategy and roadmap in close partnership with the C-suite and cross-functional leadership. Stay at the cutting edge of agentic AI (multi-modal agents, advanced reasoning models, interoperability protocols) and help establish Medeloop as a leader in transparent, compliant healthcare AI. What We're Looking For 7+ years of hands-on experience as a Machine Learning Engineer, with a proven track record building and shipping production agentic AI systems (single- or multi-agent) in industry, ideally in healthcare, life sciences, or other related domains. Experience working on analytic engines (or advanced analytics platforms) - designing, optimizing, or integrating systems that power data-driven insights, queries, or decision-making at scale. Strong theoretical foundation in ML/AI, with emphasis on NLP/LLMs, reinforcement learning, planning/reasoning algorithms. Deep expertise with agentic frameworks and tools: LangChain/LangGraph, Model Context Protocol (MCP), Agent-to-Agent (A2A) protocols, Hugging Face, PyTorch, vector databases/semantic search, prompt engineering, and observability platforms (e.g., LangSmith, Phoenix). Experience designing fully automated evaluation and testing pipelines for autonomous agents and their orchestration, including metrics for reliability, safety, factuality, cost/latency, clinical utility, and dynamic behaviors. A builder/experimenter mindset - you thrive on rapid prototyping, testing bold new ideas, iterating quickly on agent designs, and exploring uncharted territory in agentic systems. Passion for unsolved challenges in healthcare AI, with the ability to thrive in a fast-paced, multidisciplinary environment and wear multiple hats. Bonus Points Strong record in top AI/ML conferences/journals; experience with healthcare data (EHRs, claims) and regulatory considerations (HIPAA, transparency, reproducibility). Multi-cloud experience (AWS, Azure, GCP) Why Medeloop Ownership from day one: small team, high-trust, no layers between your work and its impact Technically ambitious: you'll build AI-powered workflows, not just support them Real-world stakes: your work accelerates drug development, addresses health equity, and improves clinical research for institutions that matter Strong foundation: Series A, top-tier investors, and a data asset (200M+ patient records) that most companies spend years trying to build

Job DescriptionJob DescriptionThe Role We are seeking a Staff Machine Learning Engineer with deep expertise in agentic AI - and a true passion for experimentation and creation - to design, build, test, evaluate, and productionize next-generation autonomous AI agents for healthcare and clinical research. If you love rapidly prototyping wild ideas, running build-test-learn cycles, iterating on novel agent behaviors, and turning unsolved challenges into working systems, this is the role for you. You will own end-to-end agentic workflows that reason, plan, use tools, orchestrate multi-agent collaboration, and deliver safe, reliable outcomes in highly regulated environments, while collaborating with multidisciplinary teams to influence Medeloop's technological direction. You will also be nested within a team of advisors and collaborators with deep medical and health expertise, including scientists, clinicians, and AI experts, including the former FDA commissioner, former editor of JAMA, and developer of BloombergGPT. The result: You will be an active participant in fostering a data lead public health and healthcare ecosystem. What You'll Own Lead the design and architecture of advanced agentic AI systems, including reasoning loops (ReAct, CoT, ToT), tool-calling, dynamic multi-agent orchestration, RAG pipelines, memory/state management, and emerging protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A). Build and own production-grade agent infrastructure, including prompts, function tools, workflow graphs, MCP/A2A integrations, and adaptive agent lifecycle management (spinning up, specializing, delegating, and decommissioning agents dynamically for complex healthcare workflows). Develop rigorous evaluation and safety frameworks - automated testing, benchmarking, regression testing, adversarial testing, safety guardrails, observability (tracing, logging, metrics), and human-in-the-loop mechanisms to ensure reliable, compliant performance in production. Drive LLM and ML model development - train, fine-tune, and deploy large-scale models on healthcare datasets, working closely with researchers and clinicians to solve real clinical challenges. Shape Medeloop's agentic AI strategy and roadmap in close partnership with the C-suite and cross-functional leadership. Stay at the cutting edge of agentic AI (multi-modal agents, advanced reasoning models, interoperability protocols) and help establish Medeloop as a leader in transparent, compliant healthcare AI. What We're Looking For 7+ years of hands-on experience as a Machine Learning Engineer, with a proven track record building and shipping production agentic AI systems (single- or multi-agent) in industry, ideally in healthcare, life sciences, or other related domains. Experience working on analytic engines (or advanced analytics platforms) - designing, optimizing, or integrating systems that power data-driven insights, queries, or decision-making at scale. Strong theoretical foundation in ML/AI, with emphasis on NLP/LLMs, reinforcement learning, planning/reasoning algorithms. Deep expertise with agentic frameworks and tools: LangChain/LangGraph, Model Context Protocol (MCP), Agent-to-Agent (A2A) protocols, Hugging Face, PyTorch, vector databases/semantic search, prompt engineering, and observability platforms (e.g., LangSmith, Phoenix). Experience designing fully automated evaluation and testing pipelines for autonomous agents and their orchestration, including metrics for reliability, safety, factuality, cost/latency, clinical utility, and dynamic behaviors. A builder/experimenter mindset - you thrive on rapid prototyping, testing bold new ideas, iterating quickly on agent designs, and exploring uncharted territory in agentic systems. Passion for unsolved challenges in healthcare AI, with the ability to thrive in a fast-paced, multidisciplinary environment and wear multiple hats. Bonus Points Strong record in top AI/ML conferences/journals; experience with healthcare data (EHRs, claims) and regulatory considerations (HIPAA, transparency, reproducibility). Multi-cloud experience (AWS, Azure, GCP) Why Medeloop Ownership from day one: small team, high-trust, no layers between your work and its impact Technically ambitious: you'll build AI-powered workflows, not just support them Real-world stakes: your work accelerates drug development, addresses health equity, and improves clinical research for institutions that matter Strong foundation: Series A, top-tier investors, and a data asset (200M+ patient records) that most companies spend years trying to build

Job DescriptionJob DescriptionThe Role We are seeking a Staff Machine Learning Engineer with deep expertise in agentic AI - and a true passion for experimentation and creation - to design, build, test, evaluate, and productionize next-generation autonomous AI agents for healthcare and clinical research. If you love rapidly prototyping wild ideas, running build-test-learn cycles, iterating on novel agent behaviors, and turning unsolved challenges into working systems, this is the role for you. You will own end-to-end agentic workflows that reason, plan, use tools, orchestrate multi-agent collaboration, and deliver safe, reliable outcomes in highly regulated environments, while collaborating with multidisciplinary teams to influence Medeloop's technological direction. You will also be nested within a team of advisors and collaborators with deep medical and health expertise, including scientists, clinicians, and AI experts, including the former FDA commissioner, former editor of JAMA, and developer of BloombergGPT. The result: You will be an active participant in fostering a data lead public health and healthcare ecosystem. What You'll Own Lead the design and architecture of advanced agentic AI systems, including reasoning loops (ReAct, CoT, ToT), tool-calling, dynamic multi-agent orchestration, RAG pipelines, memory/state management, and emerging protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A). Build and own production-grade agent infrastructure, including prompts, function tools, workflow graphs, MCP/A2A integrations, and adaptive agent lifecycle management (spinning up, specializing, delegating, and decommissioning agents dynamically for complex healthcare workflows). Develop rigorous evaluation and safety frameworks - automated testing, benchmarking, regression testing, adversarial testing, safety guardrails, observability (tracing, logging, metrics), and human-in-the-loop mechanisms to ensure reliable, compliant performance in production. Drive LLM and ML model development - train, fine-tune, and deploy large-scale models on healthcare datasets, working closely with researchers and clinicians to solve real clinical challenges. Shape Medeloop's agentic AI strategy and roadmap in close partnership with the C-suite and cross-functional leadership. Stay at the cutting edge of agentic AI (multi-modal agents, advanced reasoning models, interoperability protocols) and help establish Medeloop as a leader in transparent, compliant healthcare AI. What We're Looking For 7+ years of hands-on experience as a Machine Learning Engineer, with a proven track record building and shipping production agentic AI systems (single- or multi-agent) in industry, ideally in healthcare, life sciences, or other related domains. Experience working on analytic engines (or advanced analytics platforms) - designing, optimizing, or integrating systems that power data-driven insights, queries, or decision-making at scale. Strong theoretical foundation in ML/AI, with emphasis on NLP/LLMs, reinforcement learning, planning/reasoning algorithms. Deep expertise with agentic frameworks and tools: LangChain/LangGraph, Model Context Protocol (MCP), Agent-to-Agent (A2A) protocols, Hugging Face, PyTorch, vector databases/semantic search, prompt engineering, and observability platforms (e.g., LangSmith, Phoenix). Experience designing fully automated evaluation and testing pipelines for autonomous agents and their orchestration, including metrics for reliability, safety, factuality, cost/latency, clinical utility, and dynamic behaviors. A builder/experimenter mindset - you thrive on rapid prototyping, testing bold new ideas, iterating quickly on agent designs, and exploring uncharted territory in agentic systems. Passion for unsolved challenges in healthcare AI, with the ability to thrive in a fast-paced, multidisciplinary environment and wear multiple hats. Bonus Points Strong record in top AI/ML conferences/journals; experience with healthcare data (EHRs, claims) and regulatory considerations (HIPAA, transparency, reproducibility). Multi-cloud experience (AWS, Azure, GCP) Why Medeloop Ownership from day one: small team, high-trust, no layers between your work and its impact Technically ambitious: you'll build AI-powered workflows, not just support them Real-world stakes: your work accelerates drug development, addresses health equity, and improves clinical research for institutions that matter Strong foundation: Series A, top-tier investors, and a data asset (200M+ patient records) that most companies spend years trying to build

Job DescriptionJob DescriptionThe Role We are seeking a Staff Machine Learning Engineer with deep expertise in agentic AI - and a true passion for experimentation and creation - to design, build, test, evaluate, and productionize next-generation autonomous AI agents for healthcare and clinical research. If you love rapidly prototyping wild ideas, running build-test-learn cycles, iterating on novel agent behaviors, and turning unsolved challenges into working systems, this is the role for you. You will own end-to-end agentic workflows that reason, plan, use tools, orchestrate multi-agent collaboration, and deliver safe, reliable outcomes in highly regulated environments, while collaborating with multidisciplinary teams to influence Medeloop's technological direction. You will also be nested within a team of advisors and collaborators with deep medical and health expertise, including scientists, clinicians, and AI experts, including the former FDA commissioner, former editor of JAMA, and developer of BloombergGPT. The result: You will be an active participant in fostering a data lead public health and healthcare ecosystem. What You'll Own Lead the design and architecture of advanced agentic AI systems, including reasoning loops (ReAct, CoT, ToT), tool-calling, dynamic multi-agent orchestration, RAG pipelines, memory/state management, and emerging protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A). Build and own production-grade agent infrastructure, including prompts, function tools, workflow graphs, MCP/A2A integrations, and adaptive agent lifecycle management (spinning up, specializing, delegating, and decommissioning agents dynamically for complex healthcare workflows). Develop rigorous evaluation and safety frameworks - automated testing, benchmarking, regression testing, adversarial testing, safety guardrails, observability (tracing, logging, metrics), and human-in-the-loop mechanisms to ensure reliable, compliant performance in production. Drive LLM and ML model development - train, fine-tune, and deploy large-scale models on healthcare datasets, working closely with researchers and clinicians to solve real clinical challenges. Shape Medeloop's agentic AI strategy and roadmap in close partnership with the C-suite and cross-functional leadership. Stay at the cutting edge of agentic AI (multi-modal agents, advanced reasoning models, interoperability protocols) and help establish Medeloop as a leader in transparent, compliant healthcare AI. What We're Looking For 7+ years of hands-on experience as a Machine Learning Engineer, with a proven track record building and shipping production agentic AI systems (single- or multi-agent) in industry, ideally in healthcare, life sciences, or other related domains. Experience working on analytic engines (or advanced analytics platforms) - designing, optimizing, or integrating systems that power data-driven insights, queries, or decision-making at scale. Strong theoretical foundation in ML/AI, with emphasis on NLP/LLMs, reinforcement learning, planning/reasoning algorithms. Deep expertise with agentic frameworks and tools: LangChain/LangGraph, Model Context Protocol (MCP), Agent-to-Agent (A2A) protocols, Hugging Face, PyTorch, vector databases/semantic search, prompt engineering, and observability platforms (e.g., LangSmith, Phoenix). Experience designing fully automated evaluation and testing pipelines for autonomous agents and their orchestration, including metrics for reliability, safety, factuality, cost/latency, clinical utility, and dynamic behaviors. A builder/experimenter mindset - you thrive on rapid prototyping, testing bold new ideas, iterating quickly on agent designs, and exploring uncharted territory in agentic systems. Passion for unsolved challenges in healthcare AI, with the ability to thrive in a fast-paced, multidisciplinary environment and wear multiple hats. Bonus Points Strong record in top AI/ML conferences/journals; experience with healthcare data (EHRs, claims) and regulatory considerations (HIPAA, transparency, reproducibility). Multi-cloud experience (AWS, Azure, GCP) Why Medeloop Ownership from day one: small team, high-trust, no layers between your work and its impact Technically ambitious: you'll build AI-powered workflows, not just support them Real-world stakes: your work accelerates drug development, addresses health equity, and improves clinical research for institutions that matter Strong foundation: Series A, top-tier investors, and a data asset (200M+ patient records) that most companies spend years trying to build

Job DescriptionJob DescriptionThe Role We are seeking a Staff Machine Learning Engineer with deep expertise in agentic AI - and a true passion for experimentation and creation - to design, build, test, evaluate, and productionize next-generation autonomous AI agents for healthcare and clinical research. If you love rapidly prototyping wild ideas, running build-test-learn cycles, iterating on novel agent behaviors, and turning unsolved challenges into working systems, this is the role for you. You will own end-to-end agentic workflows that reason, plan, use tools, orchestrate multi-agent collaboration, and deliver safe, reliable outcomes in highly regulated environments, while collaborating with multidisciplinary teams to influence Medeloop's technological direction. You will also be nested within a team of advisors and collaborators with deep medical and health expertise, including scientists, clinicians, and AI experts, including the former FDA commissioner, former editor of JAMA, and developer of BloombergGPT. The result: You will be an active participant in fostering a data lead public health and healthcare ecosystem. What You'll Own Lead the design and architecture of advanced agentic AI systems, including reasoning loops (ReAct, CoT, ToT), tool-calling, dynamic multi-agent orchestration, RAG pipelines, memory/state management, and emerging protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A). Build and own production-grade agent infrastructure, including prompts, function tools, workflow graphs, MCP/A2A integrations, and adaptive agent lifecycle management (spinning up, specializing, delegating, and decommissioning agents dynamically for complex healthcare workflows). Develop rigorous evaluation and safety frameworks - automated testing, benchmarking, regression testing, adversarial testing, safety guardrails, observability (tracing, logging, metrics), and human-in-the-loop mechanisms to ensure reliable, compliant performance in production. Drive LLM and ML model development - train, fine-tune, and deploy large-scale models on healthcare datasets, working closely with researchers and clinicians to solve real clinical challenges. Shape Medeloop's agentic AI strategy and roadmap in close partnership with the C-suite and cross-functional leadership. Stay at the cutting edge of agentic AI (multi-modal agents, advanced reasoning models, interoperability protocols) and help establish Medeloop as a leader in transparent, compliant healthcare AI. What We're Looking For 7+ years of hands-on experience as a Machine Learning Engineer, with a proven track record building and shipping production agentic AI systems (single- or multi-agent) in industry, ideally in healthcare, life sciences, or other related domains. Experience working on analytic engines (or advanced analytics platforms) - designing, optimizing, or integrating systems that power data-driven insights, queries, or decision-making at scale. Strong theoretical foundation in ML/AI, with emphasis on NLP/LLMs, reinforcement learning, planning/reasoning algorithms. Deep expertise with agentic frameworks and tools: LangChain/LangGraph, Model Context Protocol (MCP), Agent-to-Agent (A2A) protocols, Hugging Face, PyTorch, vector databases/semantic search, prompt engineering, and observability platforms (e.g., LangSmith, Phoenix). Experience designing fully automated evaluation and testing pipelines for autonomous agents and their orchestration, including metrics for reliability, safety, factuality, cost/latency, clinical utility, and dynamic behaviors. A builder/experimenter mindset - you thrive on rapid prototyping, testing bold new ideas, iterating quickly on agent designs, and exploring uncharted territory in agentic systems. Passion for unsolved challenges in healthcare AI, with the ability to thrive in a fast-paced, multidisciplinary environment and wear multiple hats. Bonus Points Strong record in top AI/ML conferences/journals; experience with healthcare data (EHRs, claims) and regulatory considerations (HIPAA, transparency, reproducibility). Multi-cloud experience (AWS, Azure, GCP) Why Medeloop Ownership from day one: small team, high-trust, no layers between your work and its impact Technically ambitious: you'll build AI-powered workflows, not just support them Real-world stakes: your work accelerates drug development, addresses health equity, and improves clinical research for institutions that matter Strong foundation: Series A, top-tier investors, and a data asset (200M+ patient records) that most companies spend years trying to build

Job DescriptionJob DescriptionThe Role We are seeking a Staff Machine Learning Engineer with deep expertise in agentic AI - and a true passion for experimentation and creation - to design, build, test, evaluate, and productionize next-generation autonomous AI agents for healthcare and clinical research. If you love rapidly prototyping wild ideas, running build-test-learn cycles, iterating on novel agent behaviors, and turning unsolved challenges into working systems, this is the role for you. You will own end-to-end agentic workflows that reason, plan, use tools, orchestrate multi-agent collaboration, and deliver safe, reliable outcomes in highly regulated environments, while collaborating with multidisciplinary teams to influence Medeloop's technological direction. You will also be nested within a team of advisors and collaborators with deep medical and health expertise, including scientists, clinicians, and AI experts, including the former FDA commissioner, former editor of JAMA, and developer of BloombergGPT. The result: You will be an active participant in fostering a data lead public health and healthcare ecosystem. What You'll Own Lead the design and architecture of advanced agentic AI systems, including reasoning loops (ReAct, CoT, ToT), tool-calling, dynamic multi-agent orchestration, RAG pipelines, memory/state management, and emerging protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A). Build and own production-grade agent infrastructure, including prompts, function tools, workflow graphs, MCP/A2A integrations, and adaptive agent lifecycle management (spinning up, specializing, delegating, and decommissioning agents dynamically for complex healthcare workflows). Develop rigorous evaluation and safety frameworks - automated testing, benchmarking, regression testing, adversarial testing, safety guardrails, observability (tracing, logging, metrics), and human-in-the-loop mechanisms to ensure reliable, compliant performance in production. Drive LLM and ML model development - train, fine-tune, and deploy large-scale models on healthcare datasets, working closely with researchers and clinicians to solve real clinical challenges. Shape Medeloop's agentic AI strategy and roadmap in close partnership with the C-suite and cross-functional leadership. Stay at the cutting edge of agentic AI (multi-modal agents, advanced reasoning models, interoperability protocols) and help establish Medeloop as a leader in transparent, compliant healthcare AI. What We're Looking For 7+ years of hands-on experience as a Machine Learning Engineer, with a proven track record building and shipping production agentic AI systems (single- or multi-agent) in industry, ideally in healthcare, life sciences, or other related domains. Experience working on analytic engines (or advanced analytics platforms) - designing, optimizing, or integrating systems that power data-driven insights, queries, or decision-making at scale. Strong theoretical foundation in ML/AI, with emphasis on NLP/LLMs, reinforcement learning, planning/reasoning algorithms. Deep expertise with agentic frameworks and tools: LangChain/LangGraph, Model Context Protocol (MCP), Agent-to-Agent (A2A) protocols, Hugging Face, PyTorch, vector databases/semantic search, prompt engineering, and observability platforms (e.g., LangSmith, Phoenix). Experience designing fully automated evaluation and testing pipelines for autonomous agents and their orchestration, including metrics for reliability, safety, factuality, cost/latency, clinical utility, and dynamic behaviors. A builder/experimenter mindset - you thrive on rapid prototyping, testing bold new ideas, iterating quickly on agent designs, and exploring uncharted territory in agentic systems. Passion for unsolved challenges in healthcare AI, with the ability to thrive in a fast-paced, multidisciplinary environment and wear multiple hats. Bonus Points Strong record in top AI/ML conferences/journals; experience with healthcare data (EHRs, claims) and regulatory considerations (HIPAA, transparency, reproducibility). Multi-cloud experience (AWS, Azure, GCP) Why Medeloop Ownership from day one: small team, high-trust, no layers between your work and its impact Technically ambitious: you'll build AI-powered workflows, not just support them Real-world stakes: your work accelerates drug development, addresses health equity, and improves clinical research for institutions that matter Strong foundation: Series A, top-tier investors, and a data asset (200M+ patient records) that most companies spend years trying to build

Job DescriptionJob DescriptionThe Role We are seeking a Staff Machine Learning Engineer with deep expertise in agentic AI - and a true passion for experimentation and creation - to design, build, test, evaluate, and productionize next-generation autonomous AI agents for healthcare and clinical research. If you love rapidly prototyping wild ideas, running build-test-learn cycles, iterating on novel agent behaviors, and turning unsolved challenges into working systems, this is the role for you. You will own end-to-end agentic workflows that reason, plan, use tools, orchestrate multi-agent collaboration, and deliver safe, reliable outcomes in highly regulated environments, while collaborating with multidisciplinary teams to influence Medeloop's technological direction. You will also be nested within a team of advisors and collaborators with deep medical and health expertise, including scientists, clinicians, and AI experts, including the former FDA commissioner, former editor of JAMA, and developer of BloombergGPT. The result: You will be an active participant in fostering a data lead public health and healthcare ecosystem. What You'll Own Lead the design and architecture of advanced agentic AI systems, including reasoning loops (ReAct, CoT, ToT), tool-calling, dynamic multi-agent orchestration, RAG pipelines, memory/state management, and emerging protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A). Build and own production-grade agent infrastructure, including prompts, function tools, workflow graphs, MCP/A2A integrations, and adaptive agent lifecycle management (spinning up, specializing, delegating, and decommissioning agents dynamically for complex healthcare workflows). Develop rigorous evaluation and safety frameworks - automated testing, benchmarking, regression testing, adversarial testing, safety guardrails, observability (tracing, logging, metrics), and human-in-the-loop mechanisms to ensure reliable, compliant performance in production. Drive LLM and ML model development - train, fine-tune, and deploy large-scale models on healthcare datasets, working closely with researchers and clinicians to solve real clinical challenges. Shape Medeloop's agentic AI strategy and roadmap in close partnership with the C-suite and cross-functional leadership. Stay at the cutting edge of agentic AI (multi-modal agents, advanced reasoning models, interoperability protocols) and help establish Medeloop as a leader in transparent, compliant healthcare AI. What We're Looking For 7+ years of hands-on experience as a Machine Learning Engineer, with a proven track record building and shipping production agentic AI systems (single- or multi-agent) in industry, ideally in healthcare, life sciences, or other related domains. Experience working on analytic engines (or advanced analytics platforms) - designing, optimizing, or integrating systems that power data-driven insights, queries, or decision-making at scale. Strong theoretical foundation in ML/AI, with emphasis on NLP/LLMs, reinforcement learning, planning/reasoning algorithms. Deep expertise with agentic frameworks and tools: LangChain/LangGraph, Model Context Protocol (MCP), Agent-to-Agent (A2A) protocols, Hugging Face, PyTorch, vector databases/semantic search, prompt engineering, and observability platforms (e.g., LangSmith, Phoenix). Experience designing fully automated evaluation and testing pipelines for autonomous agents and their orchestration, including metrics for reliability, safety, factuality, cost/latency, clinical utility, and dynamic behaviors. A builder/experimenter mindset - you thrive on rapid prototyping, testing bold new ideas, iterating quickly on agent designs, and exploring uncharted territory in agentic systems. Passion for unsolved challenges in healthcare AI, with the ability to thrive in a fast-paced, multidisciplinary environment and wear multiple hats. Bonus Points Strong record in top AI/ML conferences/journals; experience with healthcare data (EHRs, claims) and regulatory considerations (HIPAA, transparency, reproducibility). Multi-cloud experience (AWS, Azure, GCP) Why Medeloop Ownership from day one: small team, high-trust, no layers between your work and its impact Technically ambitious: you'll build AI-powered workflows, not just support them Real-world stakes: your work accelerates drug development, addresses health equity, and improves clinical research for institutions that matter Strong foundation: Series A, top-tier investors, and a data asset (200M+ patient records) that most companies spend years trying to build

Job DescriptionJob DescriptionThe Role We are seeking a Staff Machine Learning Engineer with deep expertise in agentic AI - and a true passion for experimentation and creation - to design, build, test, evaluate, and productionize next-generation autonomous AI agents for healthcare and clinical research. If you love rapidly prototyping wild ideas, running build-test-learn cycles, iterating on novel agent behaviors, and turning unsolved challenges into working systems, this is the role for you. You will own end-to-end agentic workflows that reason, plan, use tools, orchestrate multi-agent collaboration, and deliver safe, reliable outcomes in highly regulated environments, while collaborating with multidisciplinary teams to influence Medeloop's technological direction. You will also be nested within a team of advisors and collaborators with deep medical and health expertise, including scientists, clinicians, and AI experts, including the former FDA commissioner, former editor of JAMA, and developer of BloombergGPT. The result: You will be an active participant in fostering a data lead public health and healthcare ecosystem. What You'll Own Lead the design and architecture of advanced agentic AI systems, including reasoning loops (ReAct, CoT, ToT), tool-calling, dynamic multi-agent orchestration, RAG pipelines, memory/state management, and emerging protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A). Build and own production-grade agent infrastructure, including prompts, function tools, workflow graphs, MCP/A2A integrations, and adaptive agent lifecycle management (spinning up, specializing, delegating, and decommissioning agents dynamically for complex healthcare workflows). Develop rigorous evaluation and safety frameworks - automated testing, benchmarking, regression testing, adversarial testing, safety guardrails, observability (tracing, logging, metrics), and human-in-the-loop mechanisms to ensure reliable, compliant performance in production. Drive LLM and ML model development - train, fine-tune, and deploy large-scale models on healthcare datasets, working closely with researchers and clinicians to solve real clinical challenges. Shape Medeloop's agentic AI strategy and roadmap in close partnership with the C-suite and cross-functional leadership. Stay at the cutting edge of agentic AI (multi-modal agents, advanced reasoning models, interoperability protocols) and help establish Medeloop as a leader in transparent, compliant healthcare AI. What We're Looking For 7+ years of hands-on experience as a Machine Learning Engineer, with a proven track record building and shipping production agentic AI systems (single- or multi-agent) in industry, ideally in healthcare, life sciences, or other related domains. Experience working on analytic engines (or advanced analytics platforms) - designing, optimizing, or integrating systems that power data-driven insights, queries, or decision-making at scale. Strong theoretical foundation in ML/AI, with emphasis on NLP/LLMs, reinforcement learning, planning/reasoning algorithms. Deep expertise with agentic frameworks and tools: LangChain/LangGraph, Model Context Protocol (MCP), Agent-to-Agent (A2A) protocols, Hugging Face, PyTorch, vector databases/semantic search, prompt engineering, and observability platforms (e.g., LangSmith, Phoenix). Experience designing fully automated evaluation and testing pipelines for autonomous agents and their orchestration, including metrics for reliability, safety, factuality, cost/latency, clinical utility, and dynamic behaviors. A builder/experimenter mindset - you thrive on rapid prototyping, testing bold new ideas, iterating quickly on agent designs, and exploring uncharted territory in agentic systems. Passion for unsolved challenges in healthcare AI, with the ability to thrive in a fast-paced, multidisciplinary environment and wear multiple hats. Bonus Points Strong record in top AI/ML conferences/journals; experience with healthcare data (EHRs, claims) and regulatory considerations (HIPAA, transparency, reproducibility). Multi-cloud experience (AWS, Azure, GCP) Why Medeloop Ownership from day one: small team, high-trust, no layers between your work and its impact Technically ambitious: you'll build AI-powered workflows, not just support them Real-world stakes: your work accelerates drug development, addresses health equity, and improves clinical research for institutions that matter Strong foundation: Series A, top-tier investors, and a data asset (200M+ patient records) that most companies spend years trying to build

Job DescriptionJob DescriptionThe Role We are seeking a Staff Machine Learning Engineer with deep expertise in agentic AI - and a true passion for experimentation and creation - to design, build, test, evaluate, and productionize next-generation autonomous AI agents for healthcare and clinical research. If you love rapidly prototyping wild ideas, running build-test-learn cycles, iterating on novel agent behaviors, and turning unsolved challenges into working systems, this is the role for you. You will own end-to-end agentic workflows that reason, plan, use tools, orchestrate multi-agent collaboration, and deliver safe, reliable outcomes in highly regulated environments, while collaborating with multidisciplinary teams to influence Medeloop's technological direction. You will also be nested within a team of advisors and collaborators with deep medical and health expertise, including scientists, clinicians, and AI experts, including the former FDA commissioner, former editor of JAMA, and developer of BloombergGPT. The result: You will be an active participant in fostering a data lead public health and healthcare ecosystem. What You'll Own Lead the design and architecture of advanced agentic AI systems, including reasoning loops (ReAct, CoT, ToT), tool-calling, dynamic multi-agent orchestration, RAG pipelines, memory/state management, and emerging protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A). Build and own production-grade agent infrastructure, including prompts, function tools, workflow graphs, MCP/A2A integrations, and adaptive agent lifecycle management (spinning up, specializing, delegating, and decommissioning agents dynamically for complex healthcare workflows). Develop rigorous evaluation and safety frameworks - automated testing, benchmarking, regression testing, adversarial testing, safety guardrails, observability (tracing, logging, metrics), and human-in-the-loop mechanisms to ensure reliable, compliant performance in production. Drive LLM and ML model development - train, fine-tune, and deploy large-scale models on healthcare datasets, working closely with researchers and clinicians to solve real clinical challenges. Shape Medeloop's agentic AI strategy and roadmap in close partnership with the C-suite and cross-functional leadership. Stay at the cutting edge of agentic AI (multi-modal agents, advanced reasoning models, interoperability protocols) and help establish Medeloop as a leader in transparent, compliant healthcare AI. What We're Looking For 7+ years of hands-on experience as a Machine Learning Engineer, with a proven track record building and shipping production agentic AI systems (single- or multi-agent) in industry, ideally in healthcare, life sciences, or other related domains. Experience working on analytic engines (or advanced analytics platforms) - designing, optimizing, or integrating systems that power data-driven insights, queries, or decision-making at scale. Strong theoretical foundation in ML/AI, with emphasis on NLP/LLMs, reinforcement learning, planning/reasoning algorithms. Deep expertise with agentic frameworks and tools: LangChain/LangGraph, Model Context Protocol (MCP), Agent-to-Agent (A2A) protocols, Hugging Face, PyTorch, vector databases/semantic search, prompt engineering, and observability platforms (e.g., LangSmith, Phoenix). Experience designing fully automated evaluation and testing pipelines for autonomous agents and their orchestration, including metrics for reliability, safety, factuality, cost/latency, clinical utility, and dynamic behaviors. A builder/experimenter mindset - you thrive on rapid prototyping, testing bold new ideas, iterating quickly on agent designs, and exploring uncharted territory in agentic systems. Passion for unsolved challenges in healthcare AI, with the ability to thrive in a fast-paced, multidisciplinary environment and wear multiple hats. Bonus Points Strong record in top AI/ML conferences/journals; experience with healthcare data (EHRs, claims) and regulatory considerations (HIPAA, transparency, reproducibility). Multi-cloud experience (AWS, Azure, GCP) Why Medeloop Ownership from day one: small team, high-trust, no layers between your work and its impact Technically ambitious: you'll build AI-powered workflows, not just support them Real-world stakes: your work accelerates drug development, addresses health equity, and improves clinical research for institutions that matter Strong foundation: Series A, top-tier investors, and a data asset (200M+ patient records) that most companies spend years trying to build

Job DescriptionJob DescriptionThe Role We are seeking a Staff Machine Learning Engineer with deep expertise in agentic AI - and a true passion for experimentation and creation - to design, build, test, evaluate, and productionize next-generation autonomous AI agents for healthcare and clinical research. If you love rapidly prototyping wild ideas, running build-test-learn cycles, iterating on novel agent behaviors, and turning unsolved challenges into working systems, this is the role for you. You will own end-to-end agentic workflows that reason, plan, use tools, orchestrate multi-agent collaboration, and deliver safe, reliable outcomes in highly regulated environments, while collaborating with multidisciplinary teams to influence Medeloop's technological direction. You will also be nested within a team of advisors and collaborators with deep medical and health expertise, including scientists, clinicians, and AI experts, including the former FDA commissioner, former editor of JAMA, and developer of BloombergGPT. The result: You will be an active participant in fostering a data lead public health and healthcare ecosystem. What You'll Own Lead the design and architecture of advanced agentic AI systems, including reasoning loops (ReAct, CoT, ToT), tool-calling, dynamic multi-agent orchestration, RAG pipelines, memory/state management, and emerging protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A). Build and own production-grade agent infrastructure, including prompts, function tools, workflow graphs, MCP/A2A integrations, and adaptive agent lifecycle management (spinning up, specializing, delegating, and decommissioning agents dynamically for complex healthcare workflows). Develop rigorous evaluation and safety frameworks - automated testing, benchmarking, regression testing, adversarial testing, safety guardrails, observability (tracing, logging, metrics), and human-in-the-loop mechanisms to ensure reliable, compliant performance in production. Drive LLM and ML model development - train, fine-tune, and deploy large-scale models on healthcare datasets, working closely with researchers and clinicians to solve real clinical challenges. Shape Medeloop's agentic AI strategy and roadmap in close partnership with the C-suite and cross-functional leadership. Stay at the cutting edge of agentic AI (multi-modal agents, advanced reasoning models, interoperability protocols) and help establish Medeloop as a leader in transparent, compliant healthcare AI. What We're Looking For 7+ years of hands-on experience as a Machine Learning Engineer, with a proven track record building and shipping production agentic AI systems (single- or multi-agent) in industry, ideally in healthcare, life sciences, or other related domains. Experience working on analytic engines (or advanced analytics platforms) - designing, optimizing, or integrating systems that power data-driven insights, queries, or decision-making at scale. Strong theoretical foundation in ML/AI, with emphasis on NLP/LLMs, reinforcement learning, planning/reasoning algorithms. Deep expertise with agentic frameworks and tools: LangChain/LangGraph, Model Context Protocol (MCP), Agent-to-Agent (A2A) protocols, Hugging Face, PyTorch, vector databases/semantic search, prompt engineering, and observability platforms (e.g., LangSmith, Phoenix). Experience designing fully automated evaluation and testing pipelines for autonomous agents and their orchestration, including metrics for reliability, safety, factuality, cost/latency, clinical utility, and dynamic behaviors. A builder/experimenter mindset - you thrive on rapid prototyping, testing bold new ideas, iterating quickly on agent designs, and exploring uncharted territory in agentic systems. Passion for unsolved challenges in healthcare AI, with the ability to thrive in a fast-paced, multidisciplinary environment and wear multiple hats. Bonus Points Strong record in top AI/ML conferences/journals; experience with healthcare data (EHRs, claims) and regulatory considerations (HIPAA, transparency, reproducibility). Multi-cloud experience (AWS, Azure, GCP) Why Medeloop Ownership from day one: small team, high-trust, no layers between your work and its impact Technically ambitious: you'll build AI-powered workflows, not just support them Real-world stakes: your work accelerates drug development, addresses health equity, and improves clinical research for institutions that matter Strong foundation: Series A, top-tier investors, and a data asset (200M+ patient records) that most companies spend years trying to build

Job DescriptionJob DescriptionThe Role We are seeking a Staff Machine Learning Engineer with deep expertise in agentic AI - and a true passion for experimentation and creation - to design, build, test, evaluate, and productionize next-generation autonomous AI agents for healthcare and clinical research. If you love rapidly prototyping wild ideas, running build-test-learn cycles, iterating on novel agent behaviors, and turning unsolved challenges into working systems, this is the role for you. You will own end-to-end agentic workflows that reason, plan, use tools, orchestrate multi-agent collaboration, and deliver safe, reliable outcomes in highly regulated environments, while collaborating with multidisciplinary teams to influence Medeloop's technological direction. You will also be nested within a team of advisors and collaborators with deep medical and health expertise, including scientists, clinicians, and AI experts, including the former FDA commissioner, former editor of JAMA, and developer of BloombergGPT. The result: You will be an active participant in fostering a data lead public health and healthcare ecosystem. What You'll Own Lead the design and architecture of advanced agentic AI systems, including reasoning loops (ReAct, CoT, ToT), tool-calling, dynamic multi-agent orchestration, RAG pipelines, memory/state management, and emerging protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A). Build and own production-grade agent infrastructure, including prompts, function tools, workflow graphs, MCP/A2A integrations, and adaptive agent lifecycle management (spinning up, specializing, delegating, and decommissioning agents dynamically for complex healthcare workflows). Develop rigorous evaluation and safety frameworks - automated testing, benchmarking, regression testing, adversarial testing, safety guardrails, observability (tracing, logging, metrics), and human-in-the-loop mechanisms to ensure reliable, compliant performance in production. Drive LLM and ML model development - train, fine-tune, and deploy large-scale models on healthcare datasets, working closely with researchers and clinicians to solve real clinical challenges. Shape Medeloop's agentic AI strategy and roadmap in close partnership with the C-suite and cross-functional leadership. Stay at the cutting edge of agentic AI (multi-modal agents, advanced reasoning models, interoperability protocols) and help establish Medeloop as a leader in transparent, compliant healthcare AI. What We're Looking For 7+ years of hands-on experience as a Machine Learning Engineer, with a proven track record building and shipping production agentic AI systems (single- or multi-agent) in industry, ideally in healthcare, life sciences, or other related domains. Experience working on analytic engines (or advanced analytics platforms) - designing, optimizing, or integrating systems that power data-driven insights, queries, or decision-making at scale. Strong theoretical foundation in ML/AI, with emphasis on NLP/LLMs, reinforcement learning, planning/reasoning algorithms. Deep expertise with agentic frameworks and tools: LangChain/LangGraph, Model Context Protocol (MCP), Agent-to-Agent (A2A) protocols, Hugging Face, PyTorch, vector databases/semantic search, prompt engineering, and observability platforms (e.g., LangSmith, Phoenix). Experience designing fully automated evaluation and testing pipelines for autonomous agents and their orchestration, including metrics for reliability, safety, factuality, cost/latency, clinical utility, and dynamic behaviors. A builder/experimenter mindset - you thrive on rapid prototyping, testing bold new ideas, iterating quickly on agent designs, and exploring uncharted territory in agentic systems. Passion for unsolved challenges in healthcare AI, with the ability to thrive in a fast-paced, multidisciplinary environment and wear multiple hats. Bonus Points Strong record in top AI/ML conferences/journals; experience with healthcare data (EHRs, claims) and regulatory considerations (HIPAA, transparency, reproducibility). Multi-cloud experience (AWS, Azure, GCP) Why Medeloop Ownership from day one: small team, high-trust, no layers between your work and its impact Technically ambitious: you'll build AI-powered workflows, not just support them Real-world stakes: your work accelerates drug development, addresses health equity, and improves clinical research for institutions that matter Strong foundation: Series A, top-tier investors, and a data asset (200M+ patient records) that most companies spend years trying to build

Job DescriptionJob DescriptionThe Role We are seeking a Staff Machine Learning Engineer with deep expertise in agentic AI - and a true passion for experimentation and creation - to design, build, test, evaluate, and productionize next-generation autonomous AI agents for healthcare and clinical research. If you love rapidly prototyping wild ideas, running build-test-learn cycles, iterating on novel agent behaviors, and turning unsolved challenges into working systems, this is the role for you. You will own end-to-end agentic workflows that reason, plan, use tools, orchestrate multi-agent collaboration, and deliver safe, reliable outcomes in highly regulated environments, while collaborating with multidisciplinary teams to influence Medeloop's technological direction. You will also be nested within a team of advisors and collaborators with deep medical and health expertise, including scientists, clinicians, and AI experts, including the former FDA commissioner, former editor of JAMA, and developer of BloombergGPT. The result: You will be an active participant in fostering a data lead public health and healthcare ecosystem. What You'll Own Lead the design and architecture of advanced agentic AI systems, including reasoning loops (ReAct, CoT, ToT), tool-calling, dynamic multi-agent orchestration, RAG pipelines, memory/state management, and emerging protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A). Build and own production-grade agent infrastructure, including prompts, function tools, workflow graphs, MCP/A2A integrations, and adaptive agent lifecycle management (spinning up, specializing, delegating, and decommissioning agents dynamically for complex healthcare workflows). Develop rigorous evaluation and safety frameworks - automated testing, benchmarking, regression testing, adversarial testing, safety guardrails, observability (tracing, logging, metrics), and human-in-the-loop mechanisms to ensure reliable, compliant performance in production. Drive LLM and ML model development - train, fine-tune, and deploy large-scale models on healthcare datasets, working closely with researchers and clinicians to solve real clinical challenges. Shape Medeloop's agentic AI strategy and roadmap in close partnership with the C-suite and cross-functional leadership. Stay at the cutting edge of agentic AI (multi-modal agents, advanced reasoning models, interoperability protocols) and help establish Medeloop as a leader in transparent, compliant healthcare AI. What We're Looking For 7+ years of hands-on experience as a Machine Learning Engineer, with a proven track record building and shipping production agentic AI systems (single- or multi-agent) in industry, ideally in healthcare, life sciences, or other related domains. Experience working on analytic engines (or advanced analytics platforms) - designing, optimizing, or integrating systems that power data-driven insights, queries, or decision-making at scale. Strong theoretical foundation in ML/AI, with emphasis on NLP/LLMs, reinforcement learning, planning/reasoning algorithms. Deep expertise with agentic frameworks and tools: LangChain/LangGraph, Model Context Protocol (MCP), Agent-to-Agent (A2A) protocols, Hugging Face, PyTorch, vector databases/semantic search, prompt engineering, and observability platforms (e.g., LangSmith, Phoenix). Experience designing fully automated evaluation and testing pipelines for autonomous agents and their orchestration, including metrics for reliability, safety, factuality, cost/latency, clinical utility, and dynamic behaviors. A builder/experimenter mindset - you thrive on rapid prototyping, testing bold new ideas, iterating quickly on agent designs, and exploring uncharted territory in agentic systems. Passion for unsolved challenges in healthcare AI, with the ability to thrive in a fast-paced, multidisciplinary environment and wear multiple hats. Bonus Points Strong record in top AI/ML conferences/journals; experience with healthcare data (EHRs, claims) and regulatory considerations (HIPAA, transparency, reproducibility). Multi-cloud experience (AWS, Azure, GCP) Why Medeloop Ownership from day one: small team, high-trust, no layers between your work and its impact Technically ambitious: you'll build AI-powered workflows, not just support them Real-world stakes: your work accelerates drug development, addresses health equity, and improves clinical research for institutions that matter Strong foundation: Series A, top-tier investors, and a data asset (200M+ patient records) that most companies spend years trying to build