Senior Machine Learning Engineer

About the Job

Are you passionate about shaping the future of AI by building infrastructure that ensures large language models and AI agents are safe, reliable, and aligned with human values? Red Hat's OpenShift AI team is seeking a principal ML engineer who combines deep technical expertise with a commitment to responsible AI innovation.

As a pivotal contributor to open-source projects like  Open Data Hub, KServe, TrustyAI, Kubeflow, and llama-stack, you'll be at the forefront of democratizing trustworthy AI infrastructure. These critical open-source initiatives are transforming how organizations develop, deploy, and monitor machine learning models across hybrid cloud and edge environments. Your work will directly shape the next generation of MLOps platforms, making advanced AI technologies more accessible, secure, and ethically aligned.

In today's rapidly evolving technological landscape, AI is becoming an integral part of our lives, powering everything from daily apps to complex systems in healthcare, finance, and beyond. While this is exciting, the focus on "what AI can do" has overshadowed "how it can do it safely".

Our Team’s mission is to create reliable AI systems that humans can trust. We do this by making AI safety both practical and accessible. Practical in the sense that you can implement it today, reducing complexity to facilitate adoption by developers and organizations in real-world environments, and accessible in the sense that our tools are open source and free from vendor lock-in.

What you will do

• Architect and lead development of large-scale evaluation platforms for LLMs and agents, enabling automated, reproducible, and extensible assessment of accuracy, reliability, safety, and performance across diverse domains.

• Define organizational standards and metrics for LLM/agent evaluation, covering hallucination detection, factuality, bias, robustness, interpretability, and alignment drift.

• Build platform components and APIs that allow product teams to integrate evaluation seamlessly into training, fine-tuning, deployment, and continuous monitoring workflows.

• Design automated pipelines and benchmarks for adversarial testing, red-teaming, and stress testing of LLMs and retrieval-augmented generation (RAG) systems.

• Lead initiatives in multi-dimensional evaluation, including safety (toxicity, bias, harmful outputs), grounding (retrieval correctness, source attribution), and agent behaviors (tool use, planning, trustworthiness).

• Collaborate with cross-functional stakeholders (safety, product, research, infrastructure) to translate abstract evaluation goals into measurable, system-level frameworks.

• Advance interpretability and observability, developing tools that allow teams to understand, debug, and explain LLM behaviors in production.

• Mentor engineers and establish best practices, driving adoption of evaluation-driven development across the organization.

• Influence technical roadmaps and industry direction, representing the team’s evaluation-first approach in external forums and publications

What you will bring

• 5+ years of ML engineering experience, with 3+ years focused on large-scale evaluation of transformer-based LLMs and/or agentic systems.

• Proven experience building evaluation platforms or frameworks that operate across training, deployment, and post-deployment contexts.

• Deep expertise in designing and implementing LLM evaluation metrics (factuality, hallucination detection, grounding, toxicity, robustness).

• Strong background in scalable platform engineering, including APIs, pipelines, and integrations used by multiple product teams.

• Demonstrated ability to bridge research and engineering, operationalizing safety and alignment techniques into production evaluation systems.

• Proficiency in Python, PyTorch, Hugging Face, and modern ML ops/deployment environments.

• Track record of technical leadership, including mentoring, architecture design, and defining org-wide practices.

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• Experience with multi-agent evaluation frameworks and graph-based metrics for agent interactions.

• Background in retrieval-augmented generation (RAG) evaluation (retrieval precision/recall, grounding, attribution).

• Contributions to AI safety or evaluation research in industry or academia.

• Familiarity with adversarial testing methodologies and automated red-teaming.

• Knowledge of interpretability and transparency methods for LLMs

• Advanced degree in ML/CS or related field with focus on evaluation, safety, or interpretability.

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Pay Transparency

Red Hat determines compensation based on several factors including but not limited to job location, experience, applicable skills and training, external market value, and internal pay equity. Annual salary is one component of Red Hat’s compensation package. This position may also be eligible for bonus, commission, and/or equity. For positions with Remote-US locations, the actual salary range for the position may differ based on location but will be commensurate with job duties and relevant work experience. 

About Red Hat

Red Hat is the world’s leading provider of enterprise open source software solutions, using a community-powered approach to deliver high-performing Linux, cloud, container, and Kubernetes technologies. Spread across 40+ countries, our associates work flexibly across work environments, from in-office, to office-flex, to fully remote, depending on the requirements of their role. Red Hatters are encouraged to bring their best ideas, no matter their title or tenure. We're a leader in open source because of our open and inclusive environment. We hire creative, passionate people ready to contribute their ideas, help solve complex problems, and make an impact.

Benefits
●    Comprehensive medical, dental, and vision coverage
●    Flexible Spending Account - healthcare and dependent care
●    Health Savings Account - high deductible medical plan
●    Retirement 401(k) with employer match
●    Paid time off and holidays
●    Paid parental leave plans for all new parents
●    Leave benefits including disability, paid family medical leave, and paid military leave
●    Additional benefits including employee stock purchase plan, family planning reimbursement, tuition reimbursement, transportation expense account, employee assistance program, and more! 

Note: These benefits are only applicable to full time, permanent associates at Red Hat located in the United States. 

Inclusion at Red Hat
Red Hat’s culture is built on the open source principles of transparency, collaboration, and inclusion, where the best ideas can come from anywhere and anyone. When this is realized, it empowers people from different backgrounds, perspectives, and experiences to come together to share ideas, challenge the status quo, and drive innovation. Our aspiration is that everyone experiences this culture with equal opportunity and access, and that all voices are not only heard but also celebrated. We hope you will join our celebration, and we welcome and encourage applicants from all the beautiful dimensions that compose our global village.

Equal Opportunity Policy (EEO)
Red Hat is proud to be an equal opportunity workplace and an affirmative action employer. We review applications for employment without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, citizenship, age, veteran status, genetic information, physical or mental disability, medical condition, marital status, or any other basis prohibited by law.