Senior Machine Learning Engineer AI Eval & Safety

• 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 teams evaluation-first approach in external forums and publications

• 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.

• 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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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.

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