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About This Role
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.
Research Engineer, Safety Evaluation 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
Minimum 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
Preferred Qualifications:
- 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
About Meta:
Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.
Meta is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Meta participates in the E-Verify program in certain locations, as required by law. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.
Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at [email protected].
$219,000/year to $301,000/year + bonus + equity + benefits
Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.
Salary Context
This $219K-$301K range is above the 75th percentile for Research Engineer roles in our dataset (median: $192K across 50 roles with salary data).
View full Research Engineer salary data →Role Details
About This Role
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
The role sits at a unique intersection. You need to understand the math well enough to implement novel architectures correctly, and you need the engineering chops to make them run efficiently on distributed systems. When a research scientist has a breakthrough idea, you're the person who turns it from a notebook prototype into a training pipeline that runs on 256 GPUs.
Across the 9,653 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Meta, this role fits into their broader AI and engineering organization.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What the Work Looks Like
A typical week involves: implementing a new attention mechanism from a recent paper, profiling and optimizing a training pipeline that's bottlenecked on data loading, building evaluation infrastructure for a new benchmark, debugging distributed training issues across a GPU cluster, and pair-programming with a research scientist on their latest experiment. The work is deeply technical.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
Skills Required
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Compensation Benchmarks
Research Engineer roles pay a median of $252,000 based on 419 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. Disclosed range: $219K to $301K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and AI Engineering Manager ($251,622). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Meta AI Hiring
Meta has 50 open AI roles right now. They're hiring across AI/ML Engineer, AI Safety, AI Software Engineer, Research Engineer. Positions span New York, NY, US, Menlo Park, CA, US, Santa Clara, CA, US. Compensation range: $152K - $361K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national median.
Career Path
Common paths into Research Engineer roles include Software Engineer, ML Engineer, Research Intern.
From here, career progression typically leads toward Senior Research Engineer, Research Scientist, ML Architect.
This is one of the best entry points into AI research without a PhD. Build a strong engineering portfolio with ML projects, contribute to open-source ML frameworks, and demonstrate that you can implement complex ideas correctly and efficiently. The transition to Research Scientist is possible with published first-author work, which some research engineer roles support.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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