Credit Acceptance Corporation
San Francisco, California
At Credit Acceptance, we are building on the foundation of a successful business while investing in the future through technology, data, AI, and continuous improvement. As we continue to evolve, our Team Members play an active role in improving experiences, driving meaningful change, and shaping the future of our business. We foster an environment where people are empowered to contribute ideas, develop their careers, make an impact, and help lead our continued transformation. Our Engineering teams are helping shape the future of Credit Acceptance through innovation. Team Members collaborate across the organization to solve complex challenges, build impactful solutions, challenge the status quo, and support informed business decisions. We value curiosity, continuous learning, and new ideas from people who are passionate about driving modernization and meaningful business impact. Outcomes and Activities: Own the deployment path for ML and GenAI models end to end training and inference pipelines, model registry and versioning, serving endpoints, and controlled promotion across development, QA and production. Own runtime health for models in production: monitoring, alerting, drift and quality-regression detection, latency and throughput objectives, capacity and autoscaling behavior, and incident response through to root cause and a closed corrective action. Operate the agent runtime layer. Route production agents through the enterprise AI Gateway and MCP Gateway rather than direct model and tool access, migrate existing agents onto that governed path, and keep tool surfaces scoped, versioned and least-privilege as they change. Own evaluation of agents in production, not just before release. Online scoring and behavioral monitoring, quality-regression and drift detection against pinned baselines, sampling and judge pipelines, and the release-gate mechanics that stop a regression from shipping. Build and maintain the observability and evaluation substrate other teams depend on: trace and telemetry capture including multi-turn and multi-step agent traces, logging standards, evaluation pipeline plumbing, and the data contracts underneath them. Own platform unit economics. Measure and manage cost per inference, per document and per interaction, and produce the platform and infrastructure cost analysis that informs build-versus-buy and hosting decisions. Make the paved road real. Deliver reusable pipeline templates, deployment patterns, reference implementations and internal tooling so product teams adopt the standard path because it is faster, not because it is mandated. Partner with Cloud Engineering, Data Engineering, Security and SRE so the ML platform sits inside enterprise governance, identity and observability rather than beside it. Respond to AI-specific production incidents and drive them to a closed corrective action: prompt injection attempts, rogue-agent cost spikes, data-classification exposure through a tool call, delegation abuse between agents, and model endpoint failures. Maintain the architecture documentation and system diagrams for the ML platform and keep them accurate enough to be used in design review. Mentor engineers and interns on production ML practice and raise the operating standard through design and code review rather than through rework. Competencies: The following items detail how you will be successful in this role. Customer Empathy: Customer Empathy is the ability to understand the perspectives, pain points, and experiences of customers. It involves actively putting oneself in the customer's shoes, comprehending their needs and challenges, and using that understanding to provide a better, more customer-centric experience. Engineering Excellence: Engineering Excellence is about bringing great craftsmanship and thought leadership to deliver an outstanding product that delights customers and solves for the business. This involves the pursuit and achievement of high standards, best practices, innovation, and superior solutions. One Team: A One Team mindset refers to a collaborative approach across the organization, where individuals work together seamlessly, without boundaries, as a single, cohesive team. Shared goals, open communication and mutual support create a sense of collective purpose. This enables teams to navigate challenges and pursue shared objectives more effectively. Owner's Mindset: Owner's Mindset involves adopting a set of behaviors that reflect a sense of responsibility, accountability, strategic thinking, and a proactive approach to managing your domain. As an owner, you understand the business and your domain(s) deeply and solve for the right outcome for the domain(s) and the business. Requirements Bachelor's degree in Computer Science, Engineering, Statistics or a relevant technical field with at least 7 years of relevant experience, or a Master's degree in one of those fields with at least 5 years of relevant experience. 5+ years building and operating production ML or AI systems, with direct ownership of at least two of: training or inference pipelines, model serving infrastructure, model registry and versioning, or production monitoring and alerting. Demonstrated ownership of a production ML or AI service through its full operating life: deployed it, monitored it, was paged for it, diagnosed a failure, and shipped the fix. Strong Python and SQL, with production-quality engineering practice: version control, testing, code review, and CI/CD applied to ML workloads rather than only to application code. Hands-on experience with a cloud ML platform in production. AWS and Databricks strongly preferred, including model serving, job orchestration, and a model registry or experiment tracking system such as MLflow. Working knowledge of how LLM and GenAI workloads differ operationally from traditional ML: token cost and latency behavior, caching and batching, non-deterministic output. Experience running LLM or agent applications behind a gateway or proxy layer, and able to build best practices for model routing and fallback, credential and key management, rate limiting and budget enforcement. Working knowledge of tool-calling architecture for agents, including the Model Context Protocol: what an MCP server is, how tools are scoped and authorized, and why tool access is brokered through a gateway rather than granted directly. Experience with containerization and infrastructure as code. Ability to communicate technical and non-technical trade-offs clearly in writing to an audience that includes both engineers and non-engineers. Candidates must reside in or be willing to relocate to one of the following hub locations: the greater Southfield, Michigan, area; the greater Bellevue, Washington, area; or the San Francisco Bay Area, California. Preferred Production experience with GPU-backed model serving, including autoscaling behavior under concurrent load, cold-start management and cost control. Experience with OpenTelemetry and an enterprise observability platform such as Dynatrace, including instrumenting AI workloads rather than only conventional services. Experience with model serving efficiency techniques: quantization, parameter-efficient fine-tuning, distillation or inference optimization. Experience with data governance and access control on a lakehouse platform, such as Databricks Unity Catalog. Experience operating AI systems in a regulated industry, particularly financial services, including auditability, retention and access-control requirements. Experience with agentic or multi-step AI systems in production, including tracing and debugging multi-turn behavior. Hands-on experience with a managed agent or tool gateway, such as AWS Bedrock AgentCore Gateway, and with running MCP servers in a governed environment. Familiarity with emerging agent interoperability and agent identity standards, including Agent2Agent (A2A) style agent-to-agent delegation, agent discovery and capability advertisement, and workload identity for non-human actors. Knowledge and Skills Ability to communicate complex technical information, both verbal and written, to all levels, including senior leadership. Ability to solve problems at the source by offering simple, working solutions. Responds promptly and effectively to resolve incidents, tasks, and projects. Demonstrated ability and motivation to teach others. Ability to gain the trust of others and build solid relationships across and vertically throughout the organization. Effectively prioritize and execute tasks in a high-pressure environment. Target Compensation: This position offers a competitive base salary, with a range of $184,943- $258,920. This position is eligible for an annual variable incentive of cash and equity, between 10- 20% of base salary. Actual amounts are based on individual performance. Final compensation within the range is influenced by many factors including assigned hub location, role-specific skills, depth and experience level, industry background, relevant education and certifications. This position follows a hybrid work model, offering two work-from-home days and three standard in-office days each week with the team and key stakeholders at the local office. INDENGLP Benefits Excellent benefits package that includes 401(K) match, adoption assistance, parental leave, tuition reimbursement, comprehensive medical/ dental/vision and many nonstandard benefits that make us a Great Place to Work Our Company Values: To be successful in this role, Team Members need to be: Positive by maintaining resiliency and focusing on solutions Respectful by collaborating and actively listening Insightful by cultivating innovation, accumulating business and role specific knowledge, demonstrating self-awareness and making quality decisions Direct by effectively communicating and conveying courage Earnest by taking accountability . click apply for full job details
11/10/2026
Full time
At Credit Acceptance, we are building on the foundation of a successful business while investing in the future through technology, data, AI, and continuous improvement. As we continue to evolve, our Team Members play an active role in improving experiences, driving meaningful change, and shaping the future of our business. We foster an environment where people are empowered to contribute ideas, develop their careers, make an impact, and help lead our continued transformation. Our Engineering teams are helping shape the future of Credit Acceptance through innovation. Team Members collaborate across the organization to solve complex challenges, build impactful solutions, challenge the status quo, and support informed business decisions. We value curiosity, continuous learning, and new ideas from people who are passionate about driving modernization and meaningful business impact. Outcomes and Activities: Own the deployment path for ML and GenAI models end to end training and inference pipelines, model registry and versioning, serving endpoints, and controlled promotion across development, QA and production. Own runtime health for models in production: monitoring, alerting, drift and quality-regression detection, latency and throughput objectives, capacity and autoscaling behavior, and incident response through to root cause and a closed corrective action. Operate the agent runtime layer. Route production agents through the enterprise AI Gateway and MCP Gateway rather than direct model and tool access, migrate existing agents onto that governed path, and keep tool surfaces scoped, versioned and least-privilege as they change. Own evaluation of agents in production, not just before release. Online scoring and behavioral monitoring, quality-regression and drift detection against pinned baselines, sampling and judge pipelines, and the release-gate mechanics that stop a regression from shipping. Build and maintain the observability and evaluation substrate other teams depend on: trace and telemetry capture including multi-turn and multi-step agent traces, logging standards, evaluation pipeline plumbing, and the data contracts underneath them. Own platform unit economics. Measure and manage cost per inference, per document and per interaction, and produce the platform and infrastructure cost analysis that informs build-versus-buy and hosting decisions. Make the paved road real. Deliver reusable pipeline templates, deployment patterns, reference implementations and internal tooling so product teams adopt the standard path because it is faster, not because it is mandated. Partner with Cloud Engineering, Data Engineering, Security and SRE so the ML platform sits inside enterprise governance, identity and observability rather than beside it. Respond to AI-specific production incidents and drive them to a closed corrective action: prompt injection attempts, rogue-agent cost spikes, data-classification exposure through a tool call, delegation abuse between agents, and model endpoint failures. Maintain the architecture documentation and system diagrams for the ML platform and keep them accurate enough to be used in design review. Mentor engineers and interns on production ML practice and raise the operating standard through design and code review rather than through rework. Competencies: The following items detail how you will be successful in this role. Customer Empathy: Customer Empathy is the ability to understand the perspectives, pain points, and experiences of customers. It involves actively putting oneself in the customer's shoes, comprehending their needs and challenges, and using that understanding to provide a better, more customer-centric experience. Engineering Excellence: Engineering Excellence is about bringing great craftsmanship and thought leadership to deliver an outstanding product that delights customers and solves for the business. This involves the pursuit and achievement of high standards, best practices, innovation, and superior solutions. One Team: A One Team mindset refers to a collaborative approach across the organization, where individuals work together seamlessly, without boundaries, as a single, cohesive team. Shared goals, open communication and mutual support create a sense of collective purpose. This enables teams to navigate challenges and pursue shared objectives more effectively. Owner's Mindset: Owner's Mindset involves adopting a set of behaviors that reflect a sense of responsibility, accountability, strategic thinking, and a proactive approach to managing your domain. As an owner, you understand the business and your domain(s) deeply and solve for the right outcome for the domain(s) and the business. Requirements Bachelor's degree in Computer Science, Engineering, Statistics or a relevant technical field with at least 7 years of relevant experience, or a Master's degree in one of those fields with at least 5 years of relevant experience. 5+ years building and operating production ML or AI systems, with direct ownership of at least two of: training or inference pipelines, model serving infrastructure, model registry and versioning, or production monitoring and alerting. Demonstrated ownership of a production ML or AI service through its full operating life: deployed it, monitored it, was paged for it, diagnosed a failure, and shipped the fix. Strong Python and SQL, with production-quality engineering practice: version control, testing, code review, and CI/CD applied to ML workloads rather than only to application code. Hands-on experience with a cloud ML platform in production. AWS and Databricks strongly preferred, including model serving, job orchestration, and a model registry or experiment tracking system such as MLflow. Working knowledge of how LLM and GenAI workloads differ operationally from traditional ML: token cost and latency behavior, caching and batching, non-deterministic output. Experience running LLM or agent applications behind a gateway or proxy layer, and able to build best practices for model routing and fallback, credential and key management, rate limiting and budget enforcement. Working knowledge of tool-calling architecture for agents, including the Model Context Protocol: what an MCP server is, how tools are scoped and authorized, and why tool access is brokered through a gateway rather than granted directly. Experience with containerization and infrastructure as code. Ability to communicate technical and non-technical trade-offs clearly in writing to an audience that includes both engineers and non-engineers. Candidates must reside in or be willing to relocate to one of the following hub locations: the greater Southfield, Michigan, area; the greater Bellevue, Washington, area; or the San Francisco Bay Area, California. Preferred Production experience with GPU-backed model serving, including autoscaling behavior under concurrent load, cold-start management and cost control. Experience with OpenTelemetry and an enterprise observability platform such as Dynatrace, including instrumenting AI workloads rather than only conventional services. Experience with model serving efficiency techniques: quantization, parameter-efficient fine-tuning, distillation or inference optimization. Experience with data governance and access control on a lakehouse platform, such as Databricks Unity Catalog. Experience operating AI systems in a regulated industry, particularly financial services, including auditability, retention and access-control requirements. Experience with agentic or multi-step AI systems in production, including tracing and debugging multi-turn behavior. Hands-on experience with a managed agent or tool gateway, such as AWS Bedrock AgentCore Gateway, and with running MCP servers in a governed environment. Familiarity with emerging agent interoperability and agent identity standards, including Agent2Agent (A2A) style agent-to-agent delegation, agent discovery and capability advertisement, and workload identity for non-human actors. Knowledge and Skills Ability to communicate complex technical information, both verbal and written, to all levels, including senior leadership. Ability to solve problems at the source by offering simple, working solutions. Responds promptly and effectively to resolve incidents, tasks, and projects. Demonstrated ability and motivation to teach others. Ability to gain the trust of others and build solid relationships across and vertically throughout the organization. Effectively prioritize and execute tasks in a high-pressure environment. Target Compensation: This position offers a competitive base salary, with a range of $184,943- $258,920. This position is eligible for an annual variable incentive of cash and equity, between 10- 20% of base salary. Actual amounts are based on individual performance. Final compensation within the range is influenced by many factors including assigned hub location, role-specific skills, depth and experience level, industry background, relevant education and certifications. This position follows a hybrid work model, offering two work-from-home days and three standard in-office days each week with the team and key stakeholders at the local office. INDENGLP Benefits Excellent benefits package that includes 401(K) match, adoption assistance, parental leave, tuition reimbursement, comprehensive medical/ dental/vision and many nonstandard benefits that make us a Great Place to Work Our Company Values: To be successful in this role, Team Members need to be: Positive by maintaining resiliency and focusing on solutions Respectful by collaborating and actively listening Insightful by cultivating innovation, accumulating business and role specific knowledge, demonstrating self-awareness and making quality decisions Direct by effectively communicating and conveying courage Earnest by taking accountability . click apply for full job details
Job Description Job Description Manager, AI & Research Computing Initiatives Job ID: 3743 Client: Boston University Location: Boston, MA Duration: 9-Month Contract Work Arrangement: Hybrid - Must be local to Boston, MA Schedule: 40 Hours per Week Industry: Higher Education / Artificial Intelligence / Research Computing Job Overview Sigma Inc. is seeking an experienced Manager, AI & Research Computing Initiatives for a 9-month contract opportunity supporting Boston University in Boston, MA. This leadership position will drive the strategy, planning, and development of artificial intelligence infrastructure and research computing initiatives. The ideal candidate will have a strong background in AI/ML infrastructure, High-Performance Computing (HPC), GPU computing, cloud infrastructure, MLOps, and technical team management . The Manager will lead the technical roadmap for AI-enabled research computing, anticipate emerging infrastructure requirements, and collaborate with faculty, researchers, academic departments, and IT leadership to develop scalable solutions supporting AI-intensive research projects. This role combines technical strategy, AI infrastructure planning, research computing operations, project coordination, and people management. The successful candidate will serve as a technical leader and subject matter expert, helping advance AI and machine learning capabilities across the university's research environment. Candidates must be local to the Boston, MA area and available to work in a hybrid arrangement. Key ResponsibilitiesAI Research Computing Strategy & Infrastructure Develop and drive the technical roadmap for AI-enabled research computing and infrastructure. Lead planning and capacity management for GPU computing, accelerators, and high-performance computing resources. Evaluate and recommend AI/ML infrastructure, containerized computing environments, and MLOps tools. Anticipate emerging requirements related to AI workloads, data pipelines, model training, and model-serving infrastructure. Provide technical guidance and expertise on AI infrastructure, research computing, and emerging AI technologies. Collaborate with research computing leadership to develop AI/ML strategies and infrastructure plans. Research emerging technologies, industry standards, and best practices in artificial intelligence and automation. Project & Service Coordination Coordinate daily research computing projects, service requests, and incident management activities. Monitor service delivery, identify potential delays, and escalate critical issues to leadership. Coordinate cross-functional activities between research computing teams, IT infrastructure groups, and academic research departments. Ensure researchers receive appropriate technical resources and infrastructure support. Provide operational updates and reports to Research Computing leadership. Team Leadership & Management Supervise, mentor, and develop research computing team members. Establish priorities, assign workloads, and oversee daily service delivery. Support employee performance management, professional development, hiring, and onboarding. Promote collaboration, accountability, and continuous improvement across the team. Operational Reporting & Business Process Analysis Develop annual, monthly, and ad hoc reports on computing resources, system utilization, storage services, and infrastructure capacity. Analyze operational metrics to support purchasing decisions, resource allocation, and strategic planning. Evaluate and improve research project workflows and service delivery processes. Support research computing projects involving secure research environments and regulated data systems. Collaborate on chargeback models, resource utilization, and service management processes. Training & Documentation Coordinate technical training programs for academic researchers, faculty, staff, and technical teams. Manage synchronous and asynchronous training initiatives. Coordinate the development of video tutorials, technical guides, and educational materials. Maintain and update research computing documentation, knowledge bases, and technical resources. Promote knowledge sharing and effective use of research computing services. Required Qualifications Bachelor's degree in Computer Science, Information Technology, Data Science, Engineering, or a related technical field, or equivalent professional experience. Demonstrated experience managing AI/ML infrastructure, research computing, high-performance computing, or related technical environments. Strong understanding of AI/ML infrastructure requirements, GPU computing, accelerators, and high-performance computing resources. Experience with containerized computing environments and AI/ML workflows. Knowledge of MLOps tools, machine learning infrastructure, and model deployment environments. Experience leading technical projects and coordinating cross-functional teams. Previous experience supervising, mentoring, or managing technical professionals. Strong understanding of IT infrastructure, compute resources, storage systems, and research technology environments. Experience with operational reporting, resource utilization, and business process analysis. Excellent communication and stakeholder management skills. Ability to work with faculty, researchers, academic departments, and technical leadership. Strong organizational, analytical, and problem-solving skills. Preferred Qualifications Experience working in a university, academic research, or scientific computing environment. Experience managing High-Performance Computing (HPC) environments. Hands-on knowledge of GPU clusters, NVIDIA GPUs, accelerators, and AI compute infrastructure. Experience with Kubernetes, Docker, containers, and orchestration technologies. Familiarity with MLOps platforms, machine learning pipelines, and AI model-serving infrastructure. Experience supporting AI-intensive research projects and grant-funded research initiatives. Knowledge of secure research environments, regulated research systems, and research data governance. Experience developing technical roadmaps, infrastructure strategies, and capacity planning initiatives. Experience managing research computing services and technical support teams. Technical Skills & Search Keywords AI Infrastructure AI/ML Infrastructure Artificial Intelligence Machine Learning Research Computing High-Performance Computing (HPC) GPU Computing NVIDIA GPUs GPU Clusters Accelerated Computing MLOps Kubernetes Docker Containerization Model Deployment Model Serving AI Workflows Cloud Infrastructure Data Pipelines Research Data Infrastructure Infrastructure Planning Capacity Planning Technical Roadmap HPC Management Research IT IT Operations Team Leadership Project Management Resource Utilization Secure Research Environments Ideal Candidate Job Titles Manager, AI Infrastructure AI Infrastructure Manager Research Computing Manager High-Performance Computing (HPC) Manager HPC Infrastructure Manager AI/ML Platform Manager Machine Learning Infrastructure Manager Research IT Manager Director of Research Computing AI Engineering Manager Technical Infrastructure Manager Research Computing Technical Lead Scientific Computing Manager Work Arrangement & Schedule Hybrid work arrangement. Must be local to Boston, MA. 40 hours per week. 9-month contract. Collaboration with university researchers, faculty, and IT leadership. Contract Details Job Title: Manager, AI & Research Computing Initiatives Job ID: 3743 Client: Boston University Location: Boston, MA Duration: 9 Months Work Arrangement: Hybrid - Local Boston candidates only Schedule: 40 Hours per Week
11/09/2026
Full time
Job Description Job Description Manager, AI & Research Computing Initiatives Job ID: 3743 Client: Boston University Location: Boston, MA Duration: 9-Month Contract Work Arrangement: Hybrid - Must be local to Boston, MA Schedule: 40 Hours per Week Industry: Higher Education / Artificial Intelligence / Research Computing Job Overview Sigma Inc. is seeking an experienced Manager, AI & Research Computing Initiatives for a 9-month contract opportunity supporting Boston University in Boston, MA. This leadership position will drive the strategy, planning, and development of artificial intelligence infrastructure and research computing initiatives. The ideal candidate will have a strong background in AI/ML infrastructure, High-Performance Computing (HPC), GPU computing, cloud infrastructure, MLOps, and technical team management . The Manager will lead the technical roadmap for AI-enabled research computing, anticipate emerging infrastructure requirements, and collaborate with faculty, researchers, academic departments, and IT leadership to develop scalable solutions supporting AI-intensive research projects. This role combines technical strategy, AI infrastructure planning, research computing operations, project coordination, and people management. The successful candidate will serve as a technical leader and subject matter expert, helping advance AI and machine learning capabilities across the university's research environment. Candidates must be local to the Boston, MA area and available to work in a hybrid arrangement. Key ResponsibilitiesAI Research Computing Strategy & Infrastructure Develop and drive the technical roadmap for AI-enabled research computing and infrastructure. Lead planning and capacity management for GPU computing, accelerators, and high-performance computing resources. Evaluate and recommend AI/ML infrastructure, containerized computing environments, and MLOps tools. Anticipate emerging requirements related to AI workloads, data pipelines, model training, and model-serving infrastructure. Provide technical guidance and expertise on AI infrastructure, research computing, and emerging AI technologies. Collaborate with research computing leadership to develop AI/ML strategies and infrastructure plans. Research emerging technologies, industry standards, and best practices in artificial intelligence and automation. Project & Service Coordination Coordinate daily research computing projects, service requests, and incident management activities. Monitor service delivery, identify potential delays, and escalate critical issues to leadership. Coordinate cross-functional activities between research computing teams, IT infrastructure groups, and academic research departments. Ensure researchers receive appropriate technical resources and infrastructure support. Provide operational updates and reports to Research Computing leadership. Team Leadership & Management Supervise, mentor, and develop research computing team members. Establish priorities, assign workloads, and oversee daily service delivery. Support employee performance management, professional development, hiring, and onboarding. Promote collaboration, accountability, and continuous improvement across the team. Operational Reporting & Business Process Analysis Develop annual, monthly, and ad hoc reports on computing resources, system utilization, storage services, and infrastructure capacity. Analyze operational metrics to support purchasing decisions, resource allocation, and strategic planning. Evaluate and improve research project workflows and service delivery processes. Support research computing projects involving secure research environments and regulated data systems. Collaborate on chargeback models, resource utilization, and service management processes. Training & Documentation Coordinate technical training programs for academic researchers, faculty, staff, and technical teams. Manage synchronous and asynchronous training initiatives. Coordinate the development of video tutorials, technical guides, and educational materials. Maintain and update research computing documentation, knowledge bases, and technical resources. Promote knowledge sharing and effective use of research computing services. Required Qualifications Bachelor's degree in Computer Science, Information Technology, Data Science, Engineering, or a related technical field, or equivalent professional experience. Demonstrated experience managing AI/ML infrastructure, research computing, high-performance computing, or related technical environments. Strong understanding of AI/ML infrastructure requirements, GPU computing, accelerators, and high-performance computing resources. Experience with containerized computing environments and AI/ML workflows. Knowledge of MLOps tools, machine learning infrastructure, and model deployment environments. Experience leading technical projects and coordinating cross-functional teams. Previous experience supervising, mentoring, or managing technical professionals. Strong understanding of IT infrastructure, compute resources, storage systems, and research technology environments. Experience with operational reporting, resource utilization, and business process analysis. Excellent communication and stakeholder management skills. Ability to work with faculty, researchers, academic departments, and technical leadership. Strong organizational, analytical, and problem-solving skills. Preferred Qualifications Experience working in a university, academic research, or scientific computing environment. Experience managing High-Performance Computing (HPC) environments. Hands-on knowledge of GPU clusters, NVIDIA GPUs, accelerators, and AI compute infrastructure. Experience with Kubernetes, Docker, containers, and orchestration technologies. Familiarity with MLOps platforms, machine learning pipelines, and AI model-serving infrastructure. Experience supporting AI-intensive research projects and grant-funded research initiatives. Knowledge of secure research environments, regulated research systems, and research data governance. Experience developing technical roadmaps, infrastructure strategies, and capacity planning initiatives. Experience managing research computing services and technical support teams. Technical Skills & Search Keywords AI Infrastructure AI/ML Infrastructure Artificial Intelligence Machine Learning Research Computing High-Performance Computing (HPC) GPU Computing NVIDIA GPUs GPU Clusters Accelerated Computing MLOps Kubernetes Docker Containerization Model Deployment Model Serving AI Workflows Cloud Infrastructure Data Pipelines Research Data Infrastructure Infrastructure Planning Capacity Planning Technical Roadmap HPC Management Research IT IT Operations Team Leadership Project Management Resource Utilization Secure Research Environments Ideal Candidate Job Titles Manager, AI Infrastructure AI Infrastructure Manager Research Computing Manager High-Performance Computing (HPC) Manager HPC Infrastructure Manager AI/ML Platform Manager Machine Learning Infrastructure Manager Research IT Manager Director of Research Computing AI Engineering Manager Technical Infrastructure Manager Research Computing Technical Lead Scientific Computing Manager Work Arrangement & Schedule Hybrid work arrangement. Must be local to Boston, MA. 40 hours per week. 9-month contract. Collaboration with university researchers, faculty, and IT leadership. Contract Details Job Title: Manager, AI & Research Computing Initiatives Job ID: 3743 Client: Boston University Location: Boston, MA Duration: 9 Months Work Arrangement: Hybrid - Local Boston candidates only Schedule: 40 Hours per Week
Job Description Job Description Manager, AI & Research Computing Initiatives Job ID: 3743 Client: Boston University Location: Boston, MA Duration: 9-Month Contract Work Arrangement: Hybrid - Must be local to Boston, MA Schedule: 40 Hours per Week Industry: Higher Education / Artificial Intelligence / Research Computing Job Overview Sigma Inc. is seeking an experienced Manager, AI & Research Computing Initiatives for a 9-month contract opportunity supporting Boston University in Boston, MA. This leadership position will drive the strategy, planning, and development of artificial intelligence infrastructure and research computing initiatives. The ideal candidate will have a strong background in AI/ML infrastructure, High-Performance Computing (HPC), GPU computing, cloud infrastructure, MLOps, and technical team management . The Manager will lead the technical roadmap for AI-enabled research computing, anticipate emerging infrastructure requirements, and collaborate with faculty, researchers, academic departments, and IT leadership to develop scalable solutions supporting AI-intensive research projects. This role combines technical strategy, AI infrastructure planning, research computing operations, project coordination, and people management. The successful candidate will serve as a technical leader and subject matter expert, helping advance AI and machine learning capabilities across the university's research environment. Candidates must be local to the Boston, MA area and available to work in a hybrid arrangement. Key Responsibilities AI Research Computing Strategy & Infrastructure Develop and drive the technical roadmap for AI-enabled research computing and infrastructure. Lead planning and capacity management for GPU computing, accelerators, and high-performance computing resources. Evaluate and recommend AI/ML infrastructure, containerized computing environments, and MLOps tools. Anticipate emerging requirements related to AI workloads, data pipelines, model training, and model-serving infrastructure. Provide technical guidance and expertise on AI infrastructure, research computing, and emerging AI technologies. Collaborate with research computing leadership to develop AI/ML strategies and infrastructure plans. Research emerging technologies, industry standards, and best practices in artificial intelligence and automation. Project & Service Coordination Coordinate daily research computing projects, service requests, and incident management activities. Monitor service delivery, identify potential delays, and escalate critical issues to leadership. Coordinate cross-functional activities between research computing teams, IT infrastructure groups, and academic research departments. Ensure researchers receive appropriate technical resources and infrastructure support. Provide operational updates and reports to Research Computing leadership. Team Leadership & Management Supervise, mentor, and develop research computing team members. Establish priorities, assign workloads, and oversee daily service delivery. Support employee performance management, professional development, hiring, and onboarding. Promote collaboration, accountability, and continuous improvement across the team. Operational Reporting & Business Process Analysis Develop annual, monthly, and ad hoc reports on computing resources, system utilization, storage services, and infrastructure capacity. Analyze operational metrics to support purchasing decisions, resource allocation, and strategic planning. Evaluate and improve research project workflows and service delivery processes. Support research computing projects involving secure research environments and regulated data systems. Collaborate on chargeback models, resource utilization, and service management processes. Training & Documentation Coordinate technical training programs for academic researchers, faculty, staff, and technical teams. Manage synchronous and asynchronous training initiatives. Coordinate the development of video tutorials, technical guides, and educational materials. Maintain and update research computing documentation, knowledge bases, and technical resources. Promote knowledge sharing and effective use of research computing services. Required Qualifications Bachelor's degree in Computer Science, Information Technology, Data Science, Engineering, or a related technical field, or equivalent professional experience. Demonstrated experience managing AI/ML infrastructure, research computing, high-performance computing, or related technical environments. Strong understanding of AI/ML infrastructure requirements, GPU computing, accelerators, and high-performance computing resources. Experience with containerized computing environments and AI/ML workflows. Knowledge of MLOps tools, machine learning infrastructure, and model deployment environments. Experience leading technical projects and coordinating cross-functional teams. Previous experience supervising, mentoring, or managing technical professionals. Strong understanding of IT infrastructure, compute resources, storage systems, and research technology environments. Experience with operational reporting, resource utilization, and business process analysis. Excellent communication and stakeholder management skills. Ability to work with faculty, researchers, academic departments, and technical leadership. Strong organizational, analytical, and problem-solving skills. Preferred Qualifications Experience working in a university, academic research, or scientific computing environment. Experience managing High-Performance Computing (HPC) environments. Hands-on knowledge of GPU clusters, NVIDIA GPUs, accelerators, and AI compute infrastructure. Experience with Kubernetes, Docker, containers, and orchestration technologies. Familiarity with MLOps platforms, machine learning pipelines, and AI model-serving infrastructure. Experience supporting AI-intensive research projects and grant-funded research initiatives. Knowledge of secure research environments, regulated research systems, and research data governance. Experience developing technical roadmaps, infrastructure strategies, and capacity planning initiatives. Experience managing research computing services and technical support teams. Technical Skills & Search Keywords AI Infrastructure AI/ML Infrastructure Artificial Intelligence Machine Learning Research Computing High-Performance Computing (HPC) GPU Computing NVIDIA GPUs GPU Clusters Accelerated Computing MLOps Kubernetes Docker Containerization Model Deployment Model Serving AI Workflows Cloud Infrastructure Data Pipelines Research Data Infrastructure Infrastructure Planning Capacity Planning Technical Roadmap HPC Management Research IT IT Operations Team Leadership Project Management Resource Utilization Secure Research Environments Ideal Candidate Job Titles Manager, AI Infrastructure AI Infrastructure Manager Research Computing Manager High-Performance Computing (HPC) Manager HPC Infrastructure Manager AI/ML Platform Manager Machine Learning Infrastructure Manager Research IT Manager Director of Research Computing AI Engineering Manager Technical Infrastructure Manager Research Computing Technical Lead Scientific Computing Manager Work Arrangement & Schedule Hybrid work arrangement. Must be local to Boston, MA. 40 hours per week. 9-month contract. Collaboration with university researchers, faculty, and IT leadership. Contract Details Job Title: Manager, AI & Research Computing Initiatives Job ID: 3743 Client: Boston University Location: Boston, MA Duration: 9 Months Work Arrangement: Hybrid - Local Boston candidates only Schedule: 40 Hours per Week
11/07/2026
Full time
Job Description Job Description Manager, AI & Research Computing Initiatives Job ID: 3743 Client: Boston University Location: Boston, MA Duration: 9-Month Contract Work Arrangement: Hybrid - Must be local to Boston, MA Schedule: 40 Hours per Week Industry: Higher Education / Artificial Intelligence / Research Computing Job Overview Sigma Inc. is seeking an experienced Manager, AI & Research Computing Initiatives for a 9-month contract opportunity supporting Boston University in Boston, MA. This leadership position will drive the strategy, planning, and development of artificial intelligence infrastructure and research computing initiatives. The ideal candidate will have a strong background in AI/ML infrastructure, High-Performance Computing (HPC), GPU computing, cloud infrastructure, MLOps, and technical team management . The Manager will lead the technical roadmap for AI-enabled research computing, anticipate emerging infrastructure requirements, and collaborate with faculty, researchers, academic departments, and IT leadership to develop scalable solutions supporting AI-intensive research projects. This role combines technical strategy, AI infrastructure planning, research computing operations, project coordination, and people management. The successful candidate will serve as a technical leader and subject matter expert, helping advance AI and machine learning capabilities across the university's research environment. Candidates must be local to the Boston, MA area and available to work in a hybrid arrangement. Key Responsibilities AI Research Computing Strategy & Infrastructure Develop and drive the technical roadmap for AI-enabled research computing and infrastructure. Lead planning and capacity management for GPU computing, accelerators, and high-performance computing resources. Evaluate and recommend AI/ML infrastructure, containerized computing environments, and MLOps tools. Anticipate emerging requirements related to AI workloads, data pipelines, model training, and model-serving infrastructure. Provide technical guidance and expertise on AI infrastructure, research computing, and emerging AI technologies. Collaborate with research computing leadership to develop AI/ML strategies and infrastructure plans. Research emerging technologies, industry standards, and best practices in artificial intelligence and automation. Project & Service Coordination Coordinate daily research computing projects, service requests, and incident management activities. Monitor service delivery, identify potential delays, and escalate critical issues to leadership. Coordinate cross-functional activities between research computing teams, IT infrastructure groups, and academic research departments. Ensure researchers receive appropriate technical resources and infrastructure support. Provide operational updates and reports to Research Computing leadership. Team Leadership & Management Supervise, mentor, and develop research computing team members. Establish priorities, assign workloads, and oversee daily service delivery. Support employee performance management, professional development, hiring, and onboarding. Promote collaboration, accountability, and continuous improvement across the team. Operational Reporting & Business Process Analysis Develop annual, monthly, and ad hoc reports on computing resources, system utilization, storage services, and infrastructure capacity. Analyze operational metrics to support purchasing decisions, resource allocation, and strategic planning. Evaluate and improve research project workflows and service delivery processes. Support research computing projects involving secure research environments and regulated data systems. Collaborate on chargeback models, resource utilization, and service management processes. Training & Documentation Coordinate technical training programs for academic researchers, faculty, staff, and technical teams. Manage synchronous and asynchronous training initiatives. Coordinate the development of video tutorials, technical guides, and educational materials. Maintain and update research computing documentation, knowledge bases, and technical resources. Promote knowledge sharing and effective use of research computing services. Required Qualifications Bachelor's degree in Computer Science, Information Technology, Data Science, Engineering, or a related technical field, or equivalent professional experience. Demonstrated experience managing AI/ML infrastructure, research computing, high-performance computing, or related technical environments. Strong understanding of AI/ML infrastructure requirements, GPU computing, accelerators, and high-performance computing resources. Experience with containerized computing environments and AI/ML workflows. Knowledge of MLOps tools, machine learning infrastructure, and model deployment environments. Experience leading technical projects and coordinating cross-functional teams. Previous experience supervising, mentoring, or managing technical professionals. Strong understanding of IT infrastructure, compute resources, storage systems, and research technology environments. Experience with operational reporting, resource utilization, and business process analysis. Excellent communication and stakeholder management skills. Ability to work with faculty, researchers, academic departments, and technical leadership. Strong organizational, analytical, and problem-solving skills. Preferred Qualifications Experience working in a university, academic research, or scientific computing environment. Experience managing High-Performance Computing (HPC) environments. Hands-on knowledge of GPU clusters, NVIDIA GPUs, accelerators, and AI compute infrastructure. Experience with Kubernetes, Docker, containers, and orchestration technologies. Familiarity with MLOps platforms, machine learning pipelines, and AI model-serving infrastructure. Experience supporting AI-intensive research projects and grant-funded research initiatives. Knowledge of secure research environments, regulated research systems, and research data governance. Experience developing technical roadmaps, infrastructure strategies, and capacity planning initiatives. Experience managing research computing services and technical support teams. Technical Skills & Search Keywords AI Infrastructure AI/ML Infrastructure Artificial Intelligence Machine Learning Research Computing High-Performance Computing (HPC) GPU Computing NVIDIA GPUs GPU Clusters Accelerated Computing MLOps Kubernetes Docker Containerization Model Deployment Model Serving AI Workflows Cloud Infrastructure Data Pipelines Research Data Infrastructure Infrastructure Planning Capacity Planning Technical Roadmap HPC Management Research IT IT Operations Team Leadership Project Management Resource Utilization Secure Research Environments Ideal Candidate Job Titles Manager, AI Infrastructure AI Infrastructure Manager Research Computing Manager High-Performance Computing (HPC) Manager HPC Infrastructure Manager AI/ML Platform Manager Machine Learning Infrastructure Manager Research IT Manager Director of Research Computing AI Engineering Manager Technical Infrastructure Manager Research Computing Technical Lead Scientific Computing Manager Work Arrangement & Schedule Hybrid work arrangement. Must be local to Boston, MA. 40 hours per week. 9-month contract. Collaboration with university researchers, faculty, and IT leadership. Contract Details Job Title: Manager, AI & Research Computing Initiatives Job ID: 3743 Client: Boston University Location: Boston, MA Duration: 9 Months Work Arrangement: Hybrid - Local Boston candidates only Schedule: 40 Hours per Week
Vaco is partnering with a growing education technology organization to hire a Staff / Principal AI Engineer to help define and build the next generation of AI-powered products, platforms, and internal capabilities. This is a highly visible, hands-on technical leadership role focused on bringing practical AI innovation into both customer-facing products and internal business operations. This person will serve as a technical change leader as the organization moves toward broader use of AI agents, generative AI, machine learning, and LLM-powered product experiences. The right candidate will bring strong architecture and engineering depth, but also a product mindset. This role is about building real solutions, teaching teams how to use AI effectively, securing AI-enabled workflows, and identifying where AI can improve customer experience, training products, marketing funnels, and business outcomes. What You'll Be Doing Lead the design, development, and deployment of scalable AI and machine learning solutions across product and internal business use cases Architect generative AI systems, LLM-powered workflows, agentic solutions, and AI-enabled product features Build and optimize LLMOps pipelines that support reliable deployment, evaluation, monitoring, and iteration of AI models Partner with product, architecture, data, engineering, innovation, and marketing teams to identify where AI can create measurable business impact Drive AI innovation within customer-facing products, training platforms, digital experiences, and lead conversion workflows Evaluate and implement approaches for prompt engineering, context management, embeddings, retrieval, and model optimization Design AI systems with feedback loops, automated retraining, fine-tuning, and lifecycle management where appropriate Build data pipelines and preprocessing workflows that ensure data quality, security, and regulatory alignment Provide hands-on technical leadership through architecture reviews, code contributions, proof-of-concepts, and implementation guidance Mentor AI engineers and partner with architects across application, product, CRM, and data domains Help establish standards, documentation, and best practices for responsible AI development and deployment Stay current on emerging AI trends, including AI's impact on digital marketing, Answer Engine Optimization, Generative Engine Optimization, and customer discovery behavior Required Experience 8 or more years of progressive experience across software engineering, data, analytics, machine learning, AI engineering, or related technology roles 1 or more years of experience developing and implementing analytical, AI, or machine learning applications Hands-on experience building and deploying LLM-based solutions, generative AI applications, or AI-powered product features Strong understanding of machine learning, natural language processing, large language models, embeddings, retrieval patterns, and model evaluation Experience with Python, SQL, Hugging Face, Snowflake, and modern ML or analytics tooling Experience designing AI or ML solutions in Azure or comparable cloud environments Experience with CI/CD pipelines, Docker, Kubernetes, MLflow, or similar MLOps and lifecycle management tools Ability to translate ambiguous AI opportunities into practical, scalable engineering solutions Strong product mindset with interest in customer experience, product innovation, marketing enablement, and business impact Demonstrated ability to mentor engineers, influence technical direction, and guide best practices Strong communication skills with the ability to present complex AI concepts to technical and non-technical stakeholders Bachelor's degree in Computer Science, Artificial Intelligence, or a related field preferred, or equivalent professional experience Nice to Have Experience building AI agents or agentic workflows for internal business operations Exposure to AI security, responsible AI, model governance, or securing AI-enabled systems Experience applying AI to online shopping, customer acquisition, digital marketing, or lead conversion workflows Familiarity with Answer Engine Optimization or Generative Engine Optimization concepts Experience with Azure AI services or comparable cloud AI platforms Background working in education technology, training platforms, eCommerce, or digital learning environments Experience helping organizations adopt AI tools, workflows, and operating models across multiple teams Compensation & Benefits Salary range: $170,000 to $190,000 annually Full-time role with benefits package available If you are a hands-on AI engineering leader who wants to build practical generative AI systems, shape product innovation, and help an organization move from AI experimentation to real enterprise adoption, we would welcome the opportunity to connect. Determining compensation for this role (and others) at Vaco/Highspring depends upon a wide array of factors including but not limited to the individual's skill sets, experience and training, licensure and certifications, office location and other geographic considerations, as well as other business and organizational needs. With that said, as required by local law in geographies that require salary range disclosure, Vaco/Highspring notes the salary range for the role is noted in this job posting. The individual may also be eligible for discretionary bonuses, and can participate in medical, dental, and vision benefits as well as the company's 401(k) retirement plan. Additional disclaimer: Unless otherwise noted in the job description, the position Vaco/Highspring is filing for is occupied. Please note, however, that Vaco/Highspring is regularly asked to provide talent to other organizations. By submitting to this position, you are agreeing to be included in our talent pool for future hiring for similarly qualified positions. Submissions to this position are subject to the use of AI to perform preliminary candidate screenings, focused on ensuring minimum job requirements noted in the position are satisfied. Further assessment of candidates beyond this initial phase within Vaco/Highspring will be otherwise assessed by recruiters and hiring managers. Vaco/Highspring does not have knowledge of the tools used by its clients in making final hiring decisions and cannot opine on their use of AI products. EEO Notice Vaco by Highspring is an Equal Opportunity Employer and does not discriminate against any employee or applicant for employment because of race (including but not limited to traits historically associated with race such as hair texture and hair style), color, sex (includes pregnancy or related conditions), religion or creed, national origin, citizenship, age, disability, status as a veteran, union membership, ethnicity, gender, gender identity, gender expression, sexual orientation, marital status, political affiliation, or any other protected characteristics as required by federal, state or local law. Vaco by Highspring and its parents, affiliates, and subsidiaries are committed to the full inclusion of all qualified individuals. As part of this commitment, Vaco by Highspring and its parents, affiliates, and subsidiaries 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 . Vaco by Highspring also wants all applicants to know their rights that workplace discrimination is illegal. Representation Notice By submitting to this position, you agree that you will be giving Vaco by Highspring the exclusive right to present your as a candidate for the foregoing employment opportunity. You further agree that you have represented information about yourself accurately and have not affirmatively misrepresented your qualifications. You also agree to maintain as confidential, to the fullest extent permitted by law, any information you learn from Vaco by Highspring about the position and you will limit disclosure of information about the position only to the extent necessary to perform any obligations in furtherance of your application. In exchange, Vaco by Highspring agrees to exercise reasonable efforts to represent you through all solicitation, job screening and resume dispersal. For residents of Ontario, Canada: Based on Highspring's discussions with its Client, Highspring's understanding is that this position for employment is a current vacancy (either through Highspring as a contractor or with the client directly). Privacy Notice Vaco by Highspring and its parents, affiliates, and subsidiaries ("we," "our," or "Vaco by Highspring") respects your privacy and are committed to providing transparent notice of our policies. California residents may access Vaco by Highspring HR Notice at Collection for California Applicants and Employees here. Virginia residents may access our state specific policies here. Residents of all other states may access our policies here. Canadian residents may access our policies in English here and in French here. Residents of countries governed by GDPR may access our policies here. Additionally, submissions to this position are subject to the use of AI to perform preliminary candidate screenings, focused on ensuring minimum job requirements noted in the position are satisfied. More details about Vaco by Highspring's use of AI can be found here (). Further assessment of candidates beyond this initial phase will be conducted by recruiters and hiring managers. Vaco by Highspring does not know and cannot opine on if its client's use of AI products in hiring . click apply for full job details
10/07/2026
Full time
Vaco is partnering with a growing education technology organization to hire a Staff / Principal AI Engineer to help define and build the next generation of AI-powered products, platforms, and internal capabilities. This is a highly visible, hands-on technical leadership role focused on bringing practical AI innovation into both customer-facing products and internal business operations. This person will serve as a technical change leader as the organization moves toward broader use of AI agents, generative AI, machine learning, and LLM-powered product experiences. The right candidate will bring strong architecture and engineering depth, but also a product mindset. This role is about building real solutions, teaching teams how to use AI effectively, securing AI-enabled workflows, and identifying where AI can improve customer experience, training products, marketing funnels, and business outcomes. What You'll Be Doing Lead the design, development, and deployment of scalable AI and machine learning solutions across product and internal business use cases Architect generative AI systems, LLM-powered workflows, agentic solutions, and AI-enabled product features Build and optimize LLMOps pipelines that support reliable deployment, evaluation, monitoring, and iteration of AI models Partner with product, architecture, data, engineering, innovation, and marketing teams to identify where AI can create measurable business impact Drive AI innovation within customer-facing products, training platforms, digital experiences, and lead conversion workflows Evaluate and implement approaches for prompt engineering, context management, embeddings, retrieval, and model optimization Design AI systems with feedback loops, automated retraining, fine-tuning, and lifecycle management where appropriate Build data pipelines and preprocessing workflows that ensure data quality, security, and regulatory alignment Provide hands-on technical leadership through architecture reviews, code contributions, proof-of-concepts, and implementation guidance Mentor AI engineers and partner with architects across application, product, CRM, and data domains Help establish standards, documentation, and best practices for responsible AI development and deployment Stay current on emerging AI trends, including AI's impact on digital marketing, Answer Engine Optimization, Generative Engine Optimization, and customer discovery behavior Required Experience 8 or more years of progressive experience across software engineering, data, analytics, machine learning, AI engineering, or related technology roles 1 or more years of experience developing and implementing analytical, AI, or machine learning applications Hands-on experience building and deploying LLM-based solutions, generative AI applications, or AI-powered product features Strong understanding of machine learning, natural language processing, large language models, embeddings, retrieval patterns, and model evaluation Experience with Python, SQL, Hugging Face, Snowflake, and modern ML or analytics tooling Experience designing AI or ML solutions in Azure or comparable cloud environments Experience with CI/CD pipelines, Docker, Kubernetes, MLflow, or similar MLOps and lifecycle management tools Ability to translate ambiguous AI opportunities into practical, scalable engineering solutions Strong product mindset with interest in customer experience, product innovation, marketing enablement, and business impact Demonstrated ability to mentor engineers, influence technical direction, and guide best practices Strong communication skills with the ability to present complex AI concepts to technical and non-technical stakeholders Bachelor's degree in Computer Science, Artificial Intelligence, or a related field preferred, or equivalent professional experience Nice to Have Experience building AI agents or agentic workflows for internal business operations Exposure to AI security, responsible AI, model governance, or securing AI-enabled systems Experience applying AI to online shopping, customer acquisition, digital marketing, or lead conversion workflows Familiarity with Answer Engine Optimization or Generative Engine Optimization concepts Experience with Azure AI services or comparable cloud AI platforms Background working in education technology, training platforms, eCommerce, or digital learning environments Experience helping organizations adopt AI tools, workflows, and operating models across multiple teams Compensation & Benefits Salary range: $170,000 to $190,000 annually Full-time role with benefits package available If you are a hands-on AI engineering leader who wants to build practical generative AI systems, shape product innovation, and help an organization move from AI experimentation to real enterprise adoption, we would welcome the opportunity to connect. Determining compensation for this role (and others) at Vaco/Highspring depends upon a wide array of factors including but not limited to the individual's skill sets, experience and training, licensure and certifications, office location and other geographic considerations, as well as other business and organizational needs. With that said, as required by local law in geographies that require salary range disclosure, Vaco/Highspring notes the salary range for the role is noted in this job posting. The individual may also be eligible for discretionary bonuses, and can participate in medical, dental, and vision benefits as well as the company's 401(k) retirement plan. Additional disclaimer: Unless otherwise noted in the job description, the position Vaco/Highspring is filing for is occupied. Please note, however, that Vaco/Highspring is regularly asked to provide talent to other organizations. By submitting to this position, you are agreeing to be included in our talent pool for future hiring for similarly qualified positions. Submissions to this position are subject to the use of AI to perform preliminary candidate screenings, focused on ensuring minimum job requirements noted in the position are satisfied. Further assessment of candidates beyond this initial phase within Vaco/Highspring will be otherwise assessed by recruiters and hiring managers. Vaco/Highspring does not have knowledge of the tools used by its clients in making final hiring decisions and cannot opine on their use of AI products. EEO Notice Vaco by Highspring is an Equal Opportunity Employer and does not discriminate against any employee or applicant for employment because of race (including but not limited to traits historically associated with race such as hair texture and hair style), color, sex (includes pregnancy or related conditions), religion or creed, national origin, citizenship, age, disability, status as a veteran, union membership, ethnicity, gender, gender identity, gender expression, sexual orientation, marital status, political affiliation, or any other protected characteristics as required by federal, state or local law. Vaco by Highspring and its parents, affiliates, and subsidiaries are committed to the full inclusion of all qualified individuals. As part of this commitment, Vaco by Highspring and its parents, affiliates, and subsidiaries 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 . Vaco by Highspring also wants all applicants to know their rights that workplace discrimination is illegal. Representation Notice By submitting to this position, you agree that you will be giving Vaco by Highspring the exclusive right to present your as a candidate for the foregoing employment opportunity. You further agree that you have represented information about yourself accurately and have not affirmatively misrepresented your qualifications. You also agree to maintain as confidential, to the fullest extent permitted by law, any information you learn from Vaco by Highspring about the position and you will limit disclosure of information about the position only to the extent necessary to perform any obligations in furtherance of your application. In exchange, Vaco by Highspring agrees to exercise reasonable efforts to represent you through all solicitation, job screening and resume dispersal. For residents of Ontario, Canada: Based on Highspring's discussions with its Client, Highspring's understanding is that this position for employment is a current vacancy (either through Highspring as a contractor or with the client directly). Privacy Notice Vaco by Highspring and its parents, affiliates, and subsidiaries ("we," "our," or "Vaco by Highspring") respects your privacy and are committed to providing transparent notice of our policies. California residents may access Vaco by Highspring HR Notice at Collection for California Applicants and Employees here. Virginia residents may access our state specific policies here. Residents of all other states may access our policies here. Canadian residents may access our policies in English here and in French here. Residents of countries governed by GDPR may access our policies here. Additionally, submissions to this position are subject to the use of AI to perform preliminary candidate screenings, focused on ensuring minimum job requirements noted in the position are satisfied. More details about Vaco by Highspring's use of AI can be found here (). Further assessment of candidates beyond this initial phase will be conducted by recruiters and hiring managers. Vaco by Highspring does not know and cannot opine on if its client's use of AI products in hiring . click apply for full job details
Job Description Job Description About ATI: Automated Tire (ATI) is a Series-B startup revolutionizing automotive service with innovative robotic and software technology. Founded by experienced entrepreneurs and backed by major players in the automotive and tire sectors, ATI is building the next generation of tools that make tire shops and dealership service lanes faster, safer, and smarter. If you're passionate about building products that ship into real-world environments, ATI is the place for you. Position Overview: BrakeWise is our production brake inspection product: a mobile application paired with a camera probe that technicians use to assess pad and rotor condition during live service work. The machine learning behind it is a multi-stage pipeline of segmentation and classification models that turn raw imagery into a wear assessment a shop can act on and charge for. That pipeline works, and it is an MVP. It runs on Cloud Functions, and it will not carry us to the customer volume we're signing. We're looking for a Staff MLOps Engineer to own it - to take it from a working prototype to a serving architecture that holds up under real throughput, with the latency, cost, and reliability characteristics a paying customer expects. You'll own every aspect of how our models reach production and how they get better: serving infrastructure, deployment and rollback, monitoring and drift detection, the retraining loop, and the evaluation discipline that tells us whether a new model is actually an improvement. Model accuracy here has commercial consequences - a bad wear call is either a missed repair or an unnecessary one, in front of a customer. This is also the senior cloud architecture voice on the team. You'll partner closely with our Staff Full Stack Engineer, who owns the mobile app and customer dashboard, reviewing designs and setting GCP practices across the platform rather than only within the ML stack. Responsibilities: Own the multi-stage inference pipeline (segmentors and classifiers) end to end - serving architecture, latency, throughput, reliability, and cost per inspection Re-architect the pipeline off its current Cloud Functions MVP onto infrastructure that scales: containerized inference, GPU-backed or accelerated serving where it pays for itself, queueing, batching, and autoscaling Own model deployment: versioning, staged rollout, canary and shadow evaluation, and fast rollback when a model regresses Build and own the improvement loop - field data collection, labeling workflows, dataset versioning, evaluation harnesses, and regression suites that catch quality loss before customers do Monitor model quality in production: drift detection, segmented performance analysis, and triage of real-world failures against real inspection imagery Define the metrics that matter commercially - false-positive and false-negative rates on a wear call, technician override rate, unit inference cost - and report against them Improve model performance directly: architecture selection, augmentation, hard-example mining, and quantization or distillation where latency and cost demand it Evaluate on-device versus cloud inference trade-offs for the mobile app, and own whichever path we choose Establish MLOps foundations: reproducible training, experiment tracking, CI/CD for models, and infrastructure as code Serve as the cloud architecture counterpart to the Staff Full Stack Engineer - reviewing designs, setting GCP best practices, and raising the platform's infrastructure bar Work with hardware and field operations on capture quality - lighting, focus, and probe positioning - since upstream image quality sets the ceiling on model performance Own production support for the ML stack, including incident response and on-call participation for inference availability Proactively identify technical risks and architectural trade-offs, and communicate them clearly to leadership Requirements 8+ years of professional engineering experience, including several years owning machine learning systems in production - not solely model development Demonstrated experience taking a computer vision pipeline from prototype to production scale, serving real users at meaningful volume Deep experience deploying and operating segmentation and classification models, including multi-stage pipelines where one model's output feeds the next Strong cloud infrastructure background, preferably GCP - Vertex AI, Cloud Run, GKE, Cloud Functions, Cloud SQL, Pub/Sub, and Docker Production-grade Python, and fluency with PyTorch or TensorFlow Hands-on experience with model serving and optimization - Triton, TorchServe, ONNX, TensorRT, quantization, or equivalent Experience owning deployment and support for a live system, including incident response, rollback, and on-call Experience building data and labeling pipelines with dataset versioning and reproducible evaluation Comfort with infrastructure as code (e.g., Terraform) and CI/CD automation (e.g., GitHub Actions) Sound judgment on the accuracy, latency, and cost trade-offs that determine whether an ML product is viable Excellent problem-solving, debugging, and communication skills, including with non-technical stakeholders Preferred Qualifications: On-device or edge inference experience (Core ML, TensorFlow Lite, ExecuTorch) and integration into mobile applications Active learning or human-in-the-loop labeling systems Computer vision on small, long-tail, or industrial inspection datasets rather than large public benchmarks Experience with camera and sensor integration, or working alongside hardware teams on capture quality Experience with robotics, IoT, or edge computing (ROS or similar platforms) Familiarity with automotive service, dealership operations, or DMS ecosystems Contributions to open-source projects Why Join ATI: Be part of a groundbreaking startup transforming automotive service technology Work with a team of industry veterans and top-tier robotics and software talent Own the ML platform for a product that already has paying customers - your architecture decisions set the scaling ceiling Our customers are our investors, so you'll develop and test in real service lane environments A genuine data advantage: proprietary inspection imagery from real shops that no public dataset can replicate Clear Total Addressable Market with strong pull from B2B partners Competitive salary and comprehensive benefits package Prime location in Woburn, MA with on-site parking Collaborative, low-ego, high-intensity work environment
09/26/2026
Full time
Job Description Job Description About ATI: Automated Tire (ATI) is a Series-B startup revolutionizing automotive service with innovative robotic and software technology. Founded by experienced entrepreneurs and backed by major players in the automotive and tire sectors, ATI is building the next generation of tools that make tire shops and dealership service lanes faster, safer, and smarter. If you're passionate about building products that ship into real-world environments, ATI is the place for you. Position Overview: BrakeWise is our production brake inspection product: a mobile application paired with a camera probe that technicians use to assess pad and rotor condition during live service work. The machine learning behind it is a multi-stage pipeline of segmentation and classification models that turn raw imagery into a wear assessment a shop can act on and charge for. That pipeline works, and it is an MVP. It runs on Cloud Functions, and it will not carry us to the customer volume we're signing. We're looking for a Staff MLOps Engineer to own it - to take it from a working prototype to a serving architecture that holds up under real throughput, with the latency, cost, and reliability characteristics a paying customer expects. You'll own every aspect of how our models reach production and how they get better: serving infrastructure, deployment and rollback, monitoring and drift detection, the retraining loop, and the evaluation discipline that tells us whether a new model is actually an improvement. Model accuracy here has commercial consequences - a bad wear call is either a missed repair or an unnecessary one, in front of a customer. This is also the senior cloud architecture voice on the team. You'll partner closely with our Staff Full Stack Engineer, who owns the mobile app and customer dashboard, reviewing designs and setting GCP practices across the platform rather than only within the ML stack. Responsibilities: Own the multi-stage inference pipeline (segmentors and classifiers) end to end - serving architecture, latency, throughput, reliability, and cost per inspection Re-architect the pipeline off its current Cloud Functions MVP onto infrastructure that scales: containerized inference, GPU-backed or accelerated serving where it pays for itself, queueing, batching, and autoscaling Own model deployment: versioning, staged rollout, canary and shadow evaluation, and fast rollback when a model regresses Build and own the improvement loop - field data collection, labeling workflows, dataset versioning, evaluation harnesses, and regression suites that catch quality loss before customers do Monitor model quality in production: drift detection, segmented performance analysis, and triage of real-world failures against real inspection imagery Define the metrics that matter commercially - false-positive and false-negative rates on a wear call, technician override rate, unit inference cost - and report against them Improve model performance directly: architecture selection, augmentation, hard-example mining, and quantization or distillation where latency and cost demand it Evaluate on-device versus cloud inference trade-offs for the mobile app, and own whichever path we choose Establish MLOps foundations: reproducible training, experiment tracking, CI/CD for models, and infrastructure as code Serve as the cloud architecture counterpart to the Staff Full Stack Engineer - reviewing designs, setting GCP best practices, and raising the platform's infrastructure bar Work with hardware and field operations on capture quality - lighting, focus, and probe positioning - since upstream image quality sets the ceiling on model performance Own production support for the ML stack, including incident response and on-call participation for inference availability Proactively identify technical risks and architectural trade-offs, and communicate them clearly to leadership Requirements 8+ years of professional engineering experience, including several years owning machine learning systems in production - not solely model development Demonstrated experience taking a computer vision pipeline from prototype to production scale, serving real users at meaningful volume Deep experience deploying and operating segmentation and classification models, including multi-stage pipelines where one model's output feeds the next Strong cloud infrastructure background, preferably GCP - Vertex AI, Cloud Run, GKE, Cloud Functions, Cloud SQL, Pub/Sub, and Docker Production-grade Python, and fluency with PyTorch or TensorFlow Hands-on experience with model serving and optimization - Triton, TorchServe, ONNX, TensorRT, quantization, or equivalent Experience owning deployment and support for a live system, including incident response, rollback, and on-call Experience building data and labeling pipelines with dataset versioning and reproducible evaluation Comfort with infrastructure as code (e.g., Terraform) and CI/CD automation (e.g., GitHub Actions) Sound judgment on the accuracy, latency, and cost trade-offs that determine whether an ML product is viable Excellent problem-solving, debugging, and communication skills, including with non-technical stakeholders Preferred Qualifications: On-device or edge inference experience (Core ML, TensorFlow Lite, ExecuTorch) and integration into mobile applications Active learning or human-in-the-loop labeling systems Computer vision on small, long-tail, or industrial inspection datasets rather than large public benchmarks Experience with camera and sensor integration, or working alongside hardware teams on capture quality Experience with robotics, IoT, or edge computing (ROS or similar platforms) Familiarity with automotive service, dealership operations, or DMS ecosystems Contributions to open-source projects Why Join ATI: Be part of a groundbreaking startup transforming automotive service technology Work with a team of industry veterans and top-tier robotics and software talent Own the ML platform for a product that already has paying customers - your architecture decisions set the scaling ceiling Our customers are our investors, so you'll develop and test in real service lane environments A genuine data advantage: proprietary inspection imagery from real shops that no public dataset can replicate Clear Total Addressable Market with strong pull from B2B partners Competitive salary and comprehensive benefits package Prime location in Woburn, MA with on-site parking Collaborative, low-ego, high-intensity work environment