[Skip to main content](#main-content) [OpenNash Jobs](https://jobs.opennash.com/) [Hiring report](/reports/hiring/) · [All Meta Platforms jobs](https://jobs.opennash.com/companies/meta-platforms/) · [Search jobs](/search/) Meta Platforms · Menlo Park, CA +1 more # Research Engineer, Safety Evaluation Menlo Park, CA +1 more Research & Science Listed on Meta Platforms’s official careers site. Listed as of October 18, 2026. Check that it is still open and apply there. [Apply on Meta Platforms’s careers site →](https://www.metacareers.com/jobs/1407878191449887/) [OpenNash Jobs](https://jobs.opennash.com/) / [Meta Platforms](https://jobs.opennash.com/companies/meta-platforms/) / Research Engineer, Safety Evaluation ## 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. ## Responsibilities - 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 ## 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 ## At a glance - Posted by the employer: Not stated - First seen by OpenNash: Oct 11, 2026 - Last checked: Oct 18, 2026 - Listed for: 7 days - Location: Menlo Park, CA +1 more[Apply on Meta Platforms’s careers site →](https://www.metacareers.com/jobs/1407878191449887/) ## More jobs at Meta Platforms ### [AI Research Scientist, Physical AI](https://jobs.opennash.com/companies/meta-platforms/jobs/dd00fed694d3612897a3/) Menlo Park, CA +1 more ### [Research Scientist - AI Safety Red Teaming](https://jobs.opennash.com/companies/meta-platforms/jobs/18b7da3a8159e316b3e4/) Menlo Park, CA +1 more ### [Research Scientist, Demography and Survey Sciences](https://jobs.opennash.com/companies/meta-platforms/jobs/d1e19bf702f98477e9d4/) Menlo Park, CA +1 more ### [Business Development Manager, AI Partnerships](https://jobs.opennash.com/companies/meta-platforms/jobs/6c69ecddf810f86ad0be/) Menlo Park, CA +1 more ### [Business Development Manager, Enterprise](https://jobs.opennash.com/companies/meta-platforms/jobs/fd09c7d4e59c6a48ae01/) Menlo Park, CA +1 more ## Similar jobs at other companies ### [Observability & Evaluation Engineer](https://jobs.opennash.com/companies/ntt/jobs/7cf78fd8a87812aa79a0/) NTT · Charlotte, NC, US, United States ### [Field Evaluation Engineer](https://jobs.opennash.com/companies/sgs/jobs/5864a6801c9e9af6b76e/) SGS · Suwanee, GA, United States ### [Part-time Field Evaluation Engineer](https://jobs.opennash.com/companies/sgs/jobs/8ac7a8b12a621f54f954/) SGS · Suwanee, GA, United States ### [Research Scientist / Engineer, Foundation Model Evaluation](https://jobs.opennash.com/companies/apple/jobs/6cce92c3dcfb005f56da/) Apple · Cupertino, United States of America ### [Systems Integration Test & Evaluation Engineer, Staff - Dallas, TX](https://jobs.opennash.com/companies/lockheed-martin/jobs/13aa848a7535ac2a11e3/) Lockheed Martin · Grand Prairie, TX OpenNash lists jobs from employers’ own careers sites and does not handle applications. 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