安全评估研究工程师

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;

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

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

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3+ years of industry research or research-engineering experience in ML/AI, including hands-on work with LLMs, multimodal models, or NLP.