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Reality Labs Research (RL\-R) brings together a diverse and highly interdisciplinary team of researchers and engineers to create the future of dexterous robotic manipulation.We are seeking a staff Research Engineer with deep expertise in software engineering, robotic systems integration, and machine learning. This role is highly interdisciplinary — you will own the end\-to\-end technical architecture spanning full\-stack robotics development, deployment of learned control policies, and hands\-on integration of robotic embodiments with human wearable sensing systems.As a staff individual contributor, you will set the technical direction for complex multi\-disciplinary systems, drive innovations that bridge research and production, and mentor engineers across teams. You'll collaborate closely with researchers, engineers, and designers, leveraging cutting\-edge technology and advanced research facilities.
### Research Engineer Robotics (Systems) Responsibilities:
- Architect \& Own Real\-Time Robotic Systems: Design and maintain real\-time dexterous manipulation pipelines that integrate perception, planning, and control across multiple robotic platforms. Drive architectural decisions that enable rapid research iteration at scale
- Lead Data Capture \& Retargeting Infrastructure: Architect motion capture integration, novel hardware prototypes, and human demonstration data collection systems. Build scalable processing pipelines for large multimodal datasets that enable efficient model training and real2sim transfer
- Drive ML\-Systems Integration: Deploy and iterate on learned control policies (imitation learning, MPC, reinforcement learning) within full robotic systems. Partner with research teams to bridge the gap between algorithmic advances and real\-world system performance
- Optimize Performance \& System Reliability: Own runtime performance, debug complex system behaviors across the stack, and develop interactive demos and benchmarks that demonstrate research progress
- Set Technical Direction: Identify and drive cross\-cutting technical improvements. Influence roadmap and priorities through deep system understanding and proactive problem identification
- Collaborate \& Mentor Cross\-Functionally: Work with diverse research and engineering teams to refine modules, drive end\-to\-end system improvements, and elevate the technical capabilities of the broader team
### 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, Robotics, or related technical field (or equivalent practical experience)
- 5\+ years of experience in robotics engineering, including hands\-on work with robotic platforms in industry or academic research settings
- Demonstrated experience with machine learning systems in a robotics context (e.g., learned control policies, perception models, or ML\-driven planning)
- Full\-stack systems engineering experience designing, building, and maintaining large\-scale software\-hardware systems
- Track record of driving complex, ambiguous technical projects from conception through delivery with minimal direction
- Experience communicating technical decisions and system designs to cross\-functional stakeholders across research and engineering functions (e.g., design documents, technical reviews, project proposals)
### Preferred Qualifications:
- Experience building and operating systems that bridge research exploration and reliable deployment
- Background in computer vision, imitation learning, reinforcement learning, model\-predictive control, or sim\-to\-real transfer
- Master's or Ph.D. in Robotics, Computer Science, Electrical Engineering, or related field
- Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
- Experience in dexterous manipulation, learned robotic policy deployment, or control theory applied to real hardware
- Experience designing data collection protocols and building high\-quality ML datasets at scale
- History of mentoring engineers and influencing technical direction beyond your immediate team
- Deep familiarity with robotics frameworks (e.g., ROS/ROS2\) and real\-time robotic control systems
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
- Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
### 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 accommodations\[email protected].
$183,997/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 $183K-$257K range is above the 75th percentile for Research Engineer roles in our dataset (median: $202K across 52 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 3,823 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 in Demand for This Role
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 $260,000 based on 434 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($220K) sits 15% below the category median. Disclosed range: $183K to $257K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Meta AI Hiring
Meta has 19 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer, Data Scientist, Research Scientist. Positions span Sunnyvale, CA, US, Redmond, WA, US, New York, NY, US. Compensation range: $144K - $314K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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