Meta is seeking a Research Engineer to join the Safety Evaluation team within Meta Superintelligence Labs. Our mission is to make the safety of Meta's frontier AI systems measurable — turning ambiguous notions of "safe" into rigorous, defensible metrics that model developers, product teams, and company leadership rely on to make launch decisions. Safety evaluation is the ground truth for every safety claim Meta makes. This role owns that ground truth: designing the evaluations that detect emerging risks in text, image, voice, video, and agentic systems; building the infrastructure that runs them continuously against training checkpoints and production traffic; and setting the technical direction for how safety is measured across Meta's AI portfolio. You will define measurement standards that outlast any single model generation, and your results will directly gate what ships to billions of people.

Qualifications:

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience 3+ years of industry research or research-engineering experience in ML/AI, including hands-on work with LLMs, multimodal models, or NLP Demonstrated experience setting technical direction for a large, ambiguous problem area and driving it to delivery across multiple teams Experience designing and validating evaluations or benchmarks for ML systems, including reasoning about metric reliability and failure modes Experience building production-grade or research infrastructure that must be reliable at scale — distributed systems, data pipelines, or evaluation harnesses Programming experience in Python and hands-on experience with frameworks such as PyTorch Experience communicating complex technical results to non-specialist stakeholders and decision-makers Experience translating regulatory or policy requirements into technical measurement criteria Experience evaluating LLMs across multiple languages and modalities (text, image, voice, video, reasoning, tool use) Experience operating in an on-call or production-support capacity for live training runs or safety-critical systems Experience evaluating agentic systems — multi-step tool use, autonomy, and oversight mechanisms Publications at peer-reviewed venues (e.g. ICLR, NeurIPS, ICML, ACL, CVPR, ICCV, FAccT) with a track record in evaluation, alignment, or AI safety Experience with large-scale distributed training (hundreds/thousands of GPUs) and evaluating models in-flight during training Experience with adversarial evaluation and red-teaming, including automated attack generation and jailbreak robustness measurement experience with observability, monitoring, or experiment-tracking systems PhD in Computer Science, Machine Learning, or a relevant technical field Background in statistics and experimental design