Red Hat is seeking to build infrastructure that ensures large language models and AI agents are safe, reliable, and aligned with human values. The company aims to democratize trustworthy AI infrastructure through open-source projects like Open Data Hub, KServe, TrustyAI, Kubeflow, and llama-stack, transforming how organizations develop, deploy, and monitor machine learning models across hybrid cloud and edge environments.
Requirements
- 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.
Responsibilities
- 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.
Other
- 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.
- Collaborate with cross-functional stakeholders (safety, product, research, infrastructure) to translate abstract evaluation goals into measurable, system-level frameworks.
- Work in a flexible environment that includes in-office, office-flex, and fully remote options depending on the role.
- Contribute to Red Hat’s culture of open source, transparency, collaboration, and inclusion.
