Job description

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. Set the technical strategy for safety evaluation across multiple model families and modalities, and drive it to execution across teams Design, implement, and validate novel evaluations for safety-critical behaviors — policy adherence, adversarial robustness, agentic risk, jailbreak resistance, and emerging harm categories — including for capabilities with no established benchmark Build and harden the distributed evaluation platform so that hundreds of evals run reliably and continuously against checkpoints throughout large-scale training runs Own the measurement quality bar: signal-to-noise, statistical power, saturation, contamination, and construct validity — and establish when an eval is trustworthy enough to gate a launch Create, curate, and analyze high-quality safety datasets, including adversarial, borderline, multilingual, and long-tail cases; convert real-world incidents and red-team findings into durable, repeatable safety signals Diagnose anomalous eval results mid-training-run, determine whether the cause is a model change or an infrastructure artifact, and communicate a clear answer under time pressure Own the dashboards and reporting that researchers, product partners, and leadership use to monitor safety during training and post-launch Translate evolving global safety policy and regulatory standards into concrete, testable measurement criteria, partnering with Policy, Legal, and Integrity Influence the roadmaps of partner research and product teams; mentor engineers and researchers and raise the evaluation bar across the org Represent Meta's safety evaluation methodology to internal leadership and, where appropriate, to external audiences and the research community 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

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Keywords to mirror

aimlpythonmachine learning

Platform: meta
Effort: medium