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Meta's products give people the power to build community and bring the world closer together. The Meta Health team supports this mission through the discovery, development, and delivery of clinically validated health products with the potential to improve both individual and public health outcomes, at Meta scale.We are seeking a Research Scientist, Health Generative AI Evaluation Lead to help build a Generative AI health program aimed at empowering people to live healthy lives throughout the world. This is a hands-on individual contributor role with organization-wide clinical and technical influence. You will set the evaluation strategy for Health GenAI, build the benchmarks and harnesses that back it, and partner with clinicians, AI engineers and scientists, product, and regulatory teams to connect model-level metrics to clinical meaning. You bring the experience and ability to deliver high-quality work in a fast-paced, broad-scope, product-minded environment.The impact you'll make:The mission of the Meta Health team is to improve people's health that includes but is not limited to social, mental, and physical health through innovative technology solutions. We strive to make a positive impact on individuals' lives by addressing various health challenges and promoting overall wellness. Our team is dedicated to developing cutting-edge experiences that cater to diverse health needs, with the ultimate goal of enhancing the quality of life for our users. By working together, we aim to create a healthier and happier society, one step at a time.
Research Scientist, Clinical AI Evaluation Lead Responsibilities:
- Own the evidence standard for Health GenAI launches: define the readiness bar, produce the evidence package behind each launch decision, and serve as the scientific authority that decision rests on.
- Define and lead the evaluation strategy for GenAI health products, establishing what “good,” “safe,” and “ready to ship” mean, and the evidence required to claim each.
- Design and build LLM and multimodal benchmarks for health: clinical reasoning, factuality and hallucination, safety and harm avoidance, appropriate escalation and refusal, calibration and uncertainty, and fairness across populations.
- Develop clinical confirmation methodology, pairing automated evaluations with clinician-in-the-loop adjudication, inter-rater reliability, and study designs that produce defensible evidence.
- Partner with product and AI engineering from concept through launch of Meta's Health GenAI features, defining the claim, the evidence, and the guardrails before the feature exists.
- Partner with clinical research, regulatory, and legal to align evaluation evidence with submission and documentation needs where products are on a regulated path.
- Define new research directions in health AI evaluation where no established methodology exists, and publish work that shapes how the field measures clinical generative AI.
- Set technical direction across teams, raise the scientific bar through review and mentorship, and represent Meta's position on Health AI evaluation externally through publication, benchmarks, and standards work.
- Generates entirely new research directions, works on problems of the highest complexity and ambiguity, and makes technical contributions likely to reshape how the field thinks about the area.
Minimum Qualifications:
- 10+ years of experience in AI/ML or a quantitative research field, including 3+ years designing and running evaluations of LLMs or foundation models. Experience building clinical benchmarks, rubrics, and measurement methodology (safety, factuality, calibration, and bias)
- 3+ years applying measurement science to generative AI: constructing benchmarks and eval sets, defining scoring rubrics, validating human and model-based graders, and quantifying uncertainty in model quality claims
- Experience applying machine learning or GenAI to healthcare or medical problems (particularly in electronic health records) in direct partnership with clinical domain experts, including at least one system deployed to real users via real-world clinical confirmation study
- Experience setting technical direction across organizational boundaries, influencing clinical, product, engineering, and regulatory partners
- Strong publication record at top-tier venues or high-impact peer-reviewed journals
- MD and/or PhD in Biomedical Informatics, Machine Learning, Computer Science, Statistics, or a related field, or equivalent practical experience
- Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
Preferred Qualifications:
- Experience with clinical validation, regulated medical device software (SaMD), or FDA submissions including judgment about when a product belongs on a general-wellness path versus a regulated one
- Track record of taking a system from inception through production, including defining the evaluation criteria that gated its launch
- Experience evaluating models in production at consumer scale: online metrics, safety regression across base-model updates, and adversarial testing
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].
$184,000/year to $257,000/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 $184K-$257K range is above the 75th percentile for Research Scientist roles in our dataset (median: $199K across 63 roles with salary data).
Role Details
About This Role
Research Scientists push the boundaries of what AI can do. They design experiments, develop novel architectures, publish papers, and translate research breakthroughs into production capabilities. This is where the fundamental advances happen, from attention mechanisms to diffusion models to reasoning chains.
The work is intellectually demanding and often ambiguous. You might spend months on an approach that doesn't pan out. The best research scientists combine deep mathematical intuition with engineering pragmatism. They know when to go deep on theory and when to run experiments. They read papers voraciously and can spot incremental contributions from genuine breakthroughs.
Across the 9,079 AI roles we're tracking, Research Scientist positions make up 2% of the market. At Meta, this role fits into their broader AI and engineering organization.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
What the Work Looks Like
A typical week includes: reading and discussing recent papers with your team, designing and running experiments on multi-GPU clusters, analyzing results and iterating on hypotheses, writing up findings for internal review or publication, and collaborating with engineering teams to productionize promising results. The ratio of thinking to coding is higher than in engineering roles.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
Skills in Demand for This Role
PhD strongly preferred for most roles. Deep expertise in a specific area (NLP, computer vision, reinforcement learning, multimodal) is expected. PyTorch is the standard. Publication track record matters. Strong mathematical foundations in linear algebra, probability, optimization, and information theory are assumed.
Beyond the fundamentals, companies value experience with large-scale distributed training, novel architecture design, and the ability to bridge theory and practice. Understanding of current frontier topics (reasoning, multimodal, long-context, alignment) is essential. Code quality matters more than many researchers expect. Labs want researchers who can implement their ideas cleanly.
Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
Compensation Benchmarks
Research Scientist roles pay a median of $222,200 based on 749 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. Disclosed range: $184K to $257K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Meta AI Hiring
Meta has 52 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, Research Engineer, AI Safety. Positions span New York, NY, Austin, TX, US, Burlingame, CA, US. Compensation range: $170K - $347K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into Research Scientist roles include PhD Student, Research Engineer, Postdoc.
From here, career progression typically leads toward Research Lead, Distinguished Scientist, VP of Research.
The PhD is the entry point for most paths. Choose your advisor and research area carefully since they'll define your first industry position. Publish consistently, contribute to open-source projects in your area, and build relationships at conferences. Industry research offers better compensation and compute resources than academia, but the pressure to show product impact is real.
What to Expect in Interviews
Research interviews are multi-stage: a research talk (present your best paper), technical deep-dives on your methodology, and often a 'research proposal' exercise where you design an experiment to test a hypothesis. Coding rounds test implementation ability alongside theoretical knowledge. Be prepared to implement a paper from scratch and discuss the design choices the authors made. Strong candidates can critique papers constructively and identify gaps in experimental methodology.
When evaluating opportunities: Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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