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About This Role
We are the Monetization Ranking AI Research organization, dedicated to delivering personalized ads that maximize both user utility and advertiser value. We focus on advancing AI and ML technologies for all aspects of Monetization, including ranking, retrieval, model architecture, and optimization. By consistently integrating cutting\-edge AI/ML advancements, we help Meta’s products achieve long\-term goals and have contributed tens of billions in revenue. With our growing impact, we’re seeking motivated AI specialists to join our team and drive state\-of\-the\-art research across the Monetization organization.
### Research Engineer, Monetization AI Responsibilities:
- Develop and implement large\-scale model architectures, leveraging model scaling and transfer learning techniques
- Prioritize training scalability and signal scaling to optimize model performance, efficiency, and reliability
- Develop and apply next\-generation sequence learning techniques to drive advancements in natural language processing and understanding
- Design and implement generative modeling solutions for data augmentation
- Research and develop graph\-aware large language models
- Develop and deploy AutoML pipelines
- Apply Reinforcement Learning (RL) techniques, including long\-term value optimization, RLHF, and RL4Reason
- Use causal learning to identify and understand the cause and effect of relationships across data
- Collaborate with cross\-functional teams to design and optimize ML systems, leveraging expertise in hardware\-software co\-design, including quantization, compression, and resource\-efficient AI, to drive performance improvements and efficiency gains
- Develop and implement innovative solutions for data\-related challenges, utilizing knowledge of semi/self\-supervised learning, generative techniques, sampling, debiasing, domain adaptation, continual learning, data augmentation, cold\-start, content understanding, and large language models
### Minimum Qualifications:
- Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
- 2\+ years of experience holding an industry, faculty, or government researcher or applied researcher position, or related position
- Research experience in deep learning, reinforcement learning, natural language processing, computer vision, recommendations, ranking, search, or related areas
- Programming experience in Python and hands\-on experience with frameworks such as PyTorch
- Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment
### Preferred Qualifications:
- Experience solving complex problems and comparing alternative solutions, tradeoffs, and different perspectives to determine a path forward
- First author publications at peer\-reviewed AI conferences (e.g., NeurIPS, CVPR, ICML, ICLR, ICCV, ACL)
- Experience working and communicating cross\-functionally in a team environment
- Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
- Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
- Experience taking ideas from research to production
- Master's degree or PhD in Computer Science, Computer Engineering, Artificial Intelligence, Machine Learning, or relevant technical field
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
### 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].
$154,003/year to $217,000/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 $154K-$217K range is below the median for Research Engineer roles in our dataset (median: $188K across 44 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 3,708 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 $280,000 based on 147 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($185K) sits 34% below the category median. Disclosed range: $154K to $217K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and AI Architect ($254,798). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Meta AI Hiring
Meta has 26 open AI roles right now. They're hiring across Research Engineer, AI/ML Engineer, AI Product Manager, LLM Engineer. Positions span Menlo Park, CA, US, Burlingame, CA, US, New York, NY, US. Compensation range: $181K - $356K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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