PRAGMATIKE

Job DescriptionJob Description Location: Cambridge, MA (Eastern Time / UTC -4) Relocation package available Start date: ASAP Languages: English (required) About the Role Pragmatike is hiring on behalf of a fast-growing AI startup recognized as a Top 10 GenAI company by GTM Capital, founded by MIT CSAIL researchers. We are seeking a Staff / Principal ML Ops Engineer to lead the design, implementation, and scaling of the companys ML infrastructure and production AI systems. This is a high-impact, architecture-defining role where youll work across the entire model lifecycletraining, evaluation, deployment, observability, and continuous optimization. You will partner closely with AI researchers, GPU systems engineers, backend teams, and product stakeholders to ensure the companys large-scale AI systems are robust, efficient, automated, and production-grade. This role is ideal for someone who has already built and owned ML platforms at scale and can drive strategy as well as hands-on execution. What Youll Do Architect, build, and scale the end-to-end ML Ops pipeline, including training, fine-tuning, evaluation, rollout, and monitoring. Design reliable infrastructure for model deployment, versioning, reproducibility, and orchestration across cloud and on-prem GPU clusters. Optimize compute usage across distributed systems (Kubernetes, autoscaling, caching, GPU allocation, checkpointing workflows). Lead the implementation of observability for ML systems (monitor drift, performance, throughput, reliability, cost). Build automated workflows for dataset curation, labeling, feature pipelines, evaluation, and CI/CD for ML models. Collaborate with researchers to productionize models and accelerate training/inference pipelines. Establish ML Ops best practices, internal standards, and cross-team tooling. Mentor engineers and influence architectural direction across the entire AI platform. What Were Looking For Deep hands-on experience designing and operating production ML systems at scale (Staff/Principal-level expected). Strong background in ML Ops, distributed systems, and cloud infrastructure (AWS, GCP, or Azure). Proficiency with Python and familiarity with TypeScript or Go for platform integration. Expertise in ML frameworks: PyTorch, Transformers, vLLM, Llama-factory, Megatron-LM, CUDA / GPU acceleration (practical understanding) Strong experience with containerization and orchestration (Docker, Kubernetes, Helm, autoscaling). Deep understanding of ML lifecycle workflows: training, fine-tuning, evaluation, inference, model registries. Ability to lead technical strategy, collaborate cross-functionally, and operate in fast-paced environments Bonus Points Experience deploying and operating LLMs and generative models in production at enterprise scale. Familiarity with DevOps, CI/CD, automated deployment pipelines, and infrastructure-as-code. Experience optimizing GPU clusters, scheduling, and distributed training frameworks. Prior startup experience or comfort operating with ambiguity and high ownership. Experience working with data engineering, feature pipelines, or real-time ML systems. Why This Role Will Pivot Your Career Research pedigree: MIT CSAIL founders recognized for breakthrough AI and systems contributions. Customer impact: Deploy AI solutions powering Fortune 500 clients. Industry momentum: Lab alumni have led high-value acquisitions (MosaicML Databricks, Run:AI Nvidia, W&B CoreWeave). Funding & growth: Oversubscribed seed round, next funding in 2026. Career growth & influence: Lead AI initiatives, optimize pipelines, and directly impact production AI systems at scale. Culture & autonomy: Own critical systems while collaborating with world-class engineers. Aspirational impact: Solve AI performance challenges few engineers ever face. Benefits Competitive salary & equity options Sign-on bonus Health, Dental, and Vision 401k Pragmatike is an Equal Opportunity Employer and is committed to providing equal employment opportunities to all applicants without discrimination. We recruit on behalf of our clients and prohibit discrimination and harassment based on race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.We are committed to a fair and inclusive hiring process. We process your personal data solely for recruitment purposes, in accordance with applicable privacy laws, and maintain reasonable safeguards to protect your information. Your data may be shared with our client(s) for hiring consideration, but will not be disclosed to third parties outside of the recruitment process.

Job DescriptionJob Description Location: New York, NY Start date: ASAP Languages: English (required) About the Role Pragmatike is hiring on behalf of a high-growth AI cybersecurity company backed by top-tier investors and strategic AI leaders. The company is building the security layer for the AI era, protecting enterprises against AI-powered threats such as deepfakes, smishing, and synthetic voice attacks. Following a major Series B funding round led by industry-leading AI and venture partners, the company is entering a critical growth phase. Trusted by major banks, technology companies, and healthcare organizations, they are scaling rapidly to meet enterprise demand in a $200B+ market opportunity. We are looking for a Staff Machine Learning Engineer to define and build the companys ML capabilities from the ground up. ML is central to the product vision. This role is not about incremental optimization - it is about owning the strategy, infrastructure, and execution of machine learning across the organization. There is currently no dedicated ML infrastructure or ML team. You will establish the foundations: define where ML drives product value, design production systems end-to-end, and set the technical direction for how ML evolves across the company. This is a high-impact, highly autonomous role suited for someone who has built ML systems in production and is ready to architect an ML function from zero to scale. What Youll Do Define the companys ML strategy: where ML should be applied across products, what infrastructure is required, and how to approach build vs. buy decisions. Design and build production ML systems end-to-end - including data pipelines, model training workflows, evaluation frameworks, and inference serving. Establish rigorous evaluation methodology to measure model quality, detect regressions, and support data-driven iteration. Own the data strategy: determine what data is needed, how it should be labeled, how feedback loops are structured, and how models continuously improve. Partner closely with product and backend engineers to integrate ML into customer-facing systems. Write production-quality code within the existing codebase and contribute to architectural decisions. Over time, help recruit, mentor, and lead the ML team as the function expands. What Were Looking For 8+ years of experience building ML systems in production environments. Experience standing up ML infrastructure at an early-stage startup or serving as the senior/lead ML engineer at a company. Strong software engineering fundamentals with production experience in languages such as Python, Java, or TypeScript. Experience with cloud-based ML infrastructure (e.g., SageMaker, Bedrock, Modal, Baseten, or similar platforms). Hands-on experience with ML and data frameworks such as PyTorch, TensorFlow, Spark, or equivalent tools. Comfortable working across the stack - infrastructure, backend systems, and data platforms. Demonstrated ability to mentor engineers and elevate technical standards within a team. High autonomy and ownership mindset, with the ability to define direction and execute without predefined playbooks. Bonus Points Experience building ML systems for security, fraud detection, or adversarial environments. Experience working with LLMs in production (evaluation, fine-tuning, retrieval systems, guardrails). Background in real-time inference systems or high-throughput distributed systems. Experience making strategic build vs. buy infrastructure decisions. Previous startup experience in high-growth environments. Why This Role Will Pivot Your Career Strategic AI backing: Supported by leading AI and venture investors shaping the future of AI infrastructure and cybersecurity. Founding-level impact: Build and define the ML function from zero in a company where ML is core to product value. Enterprise traction: Products already trusted by major banks, tech firms, and healthcare organizations. Massive market opportunity: Positioned in a rapidly expanding AI cybersecurity space. Leadership path: Opportunity to evolve into Head of ML as the organization scales. Ownership & autonomy: Direct influence over architecture, infrastructure, and long-term technical direction. Benefits Competitive salary & equity options Health, Dental, and Vision 401k Hybrid flexibility Pragmatike is an Equal Opportunity Employer and is committed to providing equal employment opportunities to all applicants without discrimination. We recruit on behalf of our clients and prohibit discrimination and harassment based on race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.We are committed to a fair and inclusive hiring process. We process your personal data solely for recruitment purposes, in accordance with applicable privacy laws, and maintain reasonable safeguards to protect your information. Your data may be shared with our client(s) for hiring consideration, but will not be disclosed to third parties outside of the recruitment process.

Job DescriptionJob Description Location: New York, NY Start date: ASAP Languages: English (required) About the Role Pragmatike is hiring on behalf of a high-growth AI cybersecurity company backed by top-tier investors and strategic AI leaders. The company is building the security layer for the AI era, protecting enterprises against AI-powered threats such as deepfakes, smishing, and synthetic voice attacks. Following a major Series B funding round led by industry-leading AI and venture partners, the company is entering a critical growth phase. Trusted by major banks, technology companies, and healthcare organizations, they are scaling rapidly to meet enterprise demand in a $200B+ market opportunity. We are looking for a Staff Machine Learning Engineer to define and build the companys ML capabilities from the ground up. ML is central to the product vision. This role is not about incremental optimization - it is about owning the strategy, infrastructure, and execution of machine learning across the organization. There is currently no dedicated ML infrastructure or ML team. You will establish the foundations: define where ML drives product value, design production systems end-to-end, and set the technical direction for how ML evolves across the company. This is a high-impact, highly autonomous role suited for someone who has built ML systems in production and is ready to architect an ML function from zero to scale. What Youll Do Define the companys ML strategy: where ML should be applied across products, what infrastructure is required, and how to approach build vs. buy decisions. Design and build production ML systems end-to-end - including data pipelines, model training workflows, evaluation frameworks, and inference serving. Establish rigorous evaluation methodology to measure model quality, detect regressions, and support data-driven iteration. Own the data strategy: determine what data is needed, how it should be labeled, how feedback loops are structured, and how models continuously improve. Partner closely with product and backend engineers to integrate ML into customer-facing systems. Write production-quality code within the existing codebase and contribute to architectural decisions. Over time, help recruit, mentor, and lead the ML team as the function expands. What Were Looking For 8+ years of experience building ML systems in production environments. Experience standing up ML infrastructure at an early-stage startup or serving as the senior/lead ML engineer at a company. Strong software engineering fundamentals with production experience in languages such as Python, Java, or TypeScript. Experience with cloud-based ML infrastructure (e.g., SageMaker, Bedrock, Modal, Baseten, or similar platforms). Hands-on experience with ML and data frameworks such as PyTorch, TensorFlow, Spark, or equivalent tools. Comfortable working across the stack - infrastructure, backend systems, and data platforms. Demonstrated ability to mentor engineers and elevate technical standards within a team. High autonomy and ownership mindset, with the ability to define direction and execute without predefined playbooks. Bonus Points Experience building ML systems for security, fraud detection, or adversarial environments. Experience working with LLMs in production (evaluation, fine-tuning, retrieval systems, guardrails). Background in real-time inference systems or high-throughput distributed systems. Experience making strategic build vs. buy infrastructure decisions. Previous startup experience in high-growth environments. Why This Role Will Pivot Your Career Strategic AI backing: Supported by leading AI and venture investors shaping the future of AI infrastructure and cybersecurity. Founding-level impact: Build and define the ML function from zero in a company where ML is core to product value. Enterprise traction: Products already trusted by major banks, tech firms, and healthcare organizations. Massive market opportunity: Positioned in a rapidly expanding AI cybersecurity space. Leadership path: Opportunity to evolve into Head of ML as the organization scales. Ownership & autonomy: Direct influence over architecture, infrastructure, and long-term technical direction. Benefits Competitive salary & equity options Health, Dental, and Vision 401k Hybrid flexibility Pragmatike is an Equal Opportunity Employer and is committed to providing equal employment opportunities to all applicants without discrimination. We recruit on behalf of our clients and prohibit discrimination and harassment based on race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.We are committed to a fair and inclusive hiring process. We process your personal data solely for recruitment purposes, in accordance with applicable privacy laws, and maintain reasonable safeguards to protect your information. Your data may be shared with our client(s) for hiring consideration, but will not be disclosed to third parties outside of the recruitment process.

Job DescriptionJob Description Location: New York, NY Start date: ASAP Languages: English (required) About the Role Pragmatike is hiring on behalf of a high-growth AI cybersecurity company backed by top-tier investors and strategic AI leaders. The company is building the security layer for the AI era, protecting enterprises against AI-powered threats such as deepfakes, smishing, and synthetic voice attacks. Following a major Series B funding round led by industry-leading AI and venture partners, the company is entering a critical growth phase. Trusted by major banks, technology companies, and healthcare organizations, they are scaling rapidly to meet enterprise demand in a $200B+ market opportunity. We are looking for a Staff Machine Learning Engineer to define and build the companys ML capabilities from the ground up. ML is central to the product vision. This role is not about incremental optimization - it is about owning the strategy, infrastructure, and execution of machine learning across the organization. There is currently no dedicated ML infrastructure or ML team. You will establish the foundations: define where ML drives product value, design production systems end-to-end, and set the technical direction for how ML evolves across the company. This is a high-impact, highly autonomous role suited for someone who has built ML systems in production and is ready to architect an ML function from zero to scale. What Youll Do Define the companys ML strategy: where ML should be applied across products, what infrastructure is required, and how to approach build vs. buy decisions. Design and build production ML systems end-to-end - including data pipelines, model training workflows, evaluation frameworks, and inference serving. Establish rigorous evaluation methodology to measure model quality, detect regressions, and support data-driven iteration. Own the data strategy: determine what data is needed, how it should be labeled, how feedback loops are structured, and how models continuously improve. Partner closely with product and backend engineers to integrate ML into customer-facing systems. Write production-quality code within the existing codebase and contribute to architectural decisions. Over time, help recruit, mentor, and lead the ML team as the function expands. What Were Looking For 8+ years of experience building ML systems in production environments. Experience standing up ML infrastructure at an early-stage startup or serving as the senior/lead ML engineer at a company. Strong software engineering fundamentals with production experience in languages such as Python, Java, or TypeScript. Experience with cloud-based ML infrastructure (e.g., SageMaker, Bedrock, Modal, Baseten, or similar platforms). Hands-on experience with ML and data frameworks such as PyTorch, TensorFlow, Spark, or equivalent tools. Comfortable working across the stack - infrastructure, backend systems, and data platforms. Demonstrated ability to mentor engineers and elevate technical standards within a team. High autonomy and ownership mindset, with the ability to define direction and execute without predefined playbooks. Bonus Points Experience building ML systems for security, fraud detection, or adversarial environments. Experience working with LLMs in production (evaluation, fine-tuning, retrieval systems, guardrails). Background in real-time inference systems or high-throughput distributed systems. Experience making strategic build vs. buy infrastructure decisions. Previous startup experience in high-growth environments. Why This Role Will Pivot Your Career Strategic AI backing: Supported by leading AI and venture investors shaping the future of AI infrastructure and cybersecurity. Founding-level impact: Build and define the ML function from zero in a company where ML is core to product value. Enterprise traction: Products already trusted by major banks, tech firms, and healthcare organizations. Massive market opportunity: Positioned in a rapidly expanding AI cybersecurity space. Leadership path: Opportunity to evolve into Head of ML as the organization scales. Ownership & autonomy: Direct influence over architecture, infrastructure, and long-term technical direction. Benefits Competitive salary & equity options Health, Dental, and Vision 401k Hybrid flexibility Pragmatike is an Equal Opportunity Employer and is committed to providing equal employment opportunities to all applicants without discrimination. We recruit on behalf of our clients and prohibit discrimination and harassment based on race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.We are committed to a fair and inclusive hiring process. We process your personal data solely for recruitment purposes, in accordance with applicable privacy laws, and maintain reasonable safeguards to protect your information. Your data may be shared with our client(s) for hiring consideration, but will not be disclosed to third parties outside of the recruitment process.