Interested in this Research Engineer role at GitHub?
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About This Role
About GitHub: GitHub is the world’s leading platform for agentic software development — powered by Copilot to build, scale, and deliver secure software. Over 180 million developers, including more than 90% of the Fortune 100 companies, use GitHub to collaborate, and more than 77,000 organisations have adopted GitHub Copilot.
Locations: In this role you can work from Remote, United States
Overview:
GitHub has changed the way software is built, and we have a unique opportunity to look further ahead to identify how software development can be faster, safer, easier, and more accessible. We’re looking for talented, experienced polymaths to join us in this mission! This is the rare role that affords both startup\-level agency and a larger company’s resources.
GitHub Next has incubated genre\-defining products like Copilot, Copilot Workspace, Spark, Agentic Workflows, and more. You’ll work closely with a small group of experienced and talented researchers to explore the future of software development. Each exploration represents a risky bet that GitHub might want to make, and we have to support those bets with working prototypes. We then need to take those prototypes to market ourselves, and find evidence of product\-market fit. Our prototypes (and the evidence we gather from the marketplace) inform GitHub’s leadership and roadmap.
Please note that this is not an academic research role. Our job is to dream big about the future of software development and then build it. Our team feels like a permanent startup: every time we succeed, return to the drawing board to do it all over again.
You can see many of our projects at githubnext.com.
Responsibilities:
Problem Framing and Solution Implementation: Research Engineers are makers who turn ambitious ideas into reliable prototypes. You will take loosely defined concepts and figure out how to make them real, scoping bets wisely and delivering value quickly. You will push AI capability limits, exploring what’s almost possible today and anticipating what will be common soon.
Data Preparation and Feature Identification:Exploration spans many technologies, requiring comfort reading source code, picking up new stacks, and identifying the technical pieces needed to build prototypes. You will operate as a generalist with deeper knowledge in some areas; hybrids thrive here, though specialists are also considered. You will help shape the data, signals, and features needed to support evolving prototypes.
Coordination\-: GitHub Next runs on ideas, and strong communication drives team health and execution. You will collaborate to determine what work needs doing, split responsibilities, and move projects forward. Applies deep understanding of research approaches used across the team, organization, and industry to leverage (and not reinvent) solutions. Brings new technology and approaches into production by applying long\-term research efforts to solve immediate product needs.
Providing Consultation \& Expertise:Builds and develops collaborative relationships within and outside the organization to share expertise and create business impact. Acts as a subject matter expert and provides consultative expertise to individuals across the organization in ascertaining technical feasibility of AI ideas/opportunities.
Product and Strategy:Assesses feasibility, builds small prototypes to prove viability, and engages in end\-to\-end AI development lifecycle (e.g., researching, prototyping, minimum viable product, product, improvement iterations, and maintenance). Provides guidance to less experienced team members conducting open\-ended exploration without clear specs or pre\-determined scope to inform feasibility considerationsAs agents take over more code generation, your value comes from judgment, creativity, and shaping high‑impact ideas.
Qualifications:
Required Qualifications:* 8\+ years experience in software development, applied science, machine learning, or related field
+ OR Bachelor's Degree in Computer Science, Software Development, Electrical or Computer Engineering, Mathematical Sciences, or related field AND 6\+ years experience in software development, applied science, machine learning, or related field o OR Master's Degree in Machine Learning, Computer Science, Software Development, Electrical or Computer Engineering, Mathematical Sciences, or related field AND 4\+ years experience in software development, applied science, machine learning, or related field
+ OR Doctorate in Machine Learning, Computer Science, Software Development, Electrical or Computer Engineering, Mathematical Sciences, or related field AND 2\+ years experience in software development, applied science, machine learning, or related field
+ OR equivalent experience.
- Experience creating rapid prototypes that demonstrate a concept to stakeholders and enable decision making.
- Experience identifying and justifying research goals in situations of ambiguity and executing towards those goals without explicit direction.
Preferred Qualifications:* A deep understanding of GitHub’s industry and business, especially as it is affected by AI. Has experience communicating about these topics in a way that creates clarity and supplies nuance in a noisy marketplace.
- Has held roles that deal with topics at the boundary of human knowledge regarding software development and developers.
- Have significant experience with one of the many technology topics that are relevant to an interdisciplinary innovation group: frontend technologies (react/typescript/etc), backend technologies (infra/datastores/devops/security), ML \& AI (prompts/retrieval/agentic systems/etc), programming languages, human\-computer interaction, distributed systems.
- Have significant experience with open\-source software, communities, and the systems these communities use to self\-organize and ship software for others.
Compensation Range: The base salary range for this job is USD $140,400\.00 \- USD $372,300\.00 /Yr.
These pay ranges are intended to cover roles based across the United States. An individual's base pay depends on various factors including geographical location and review of experience, knowledge, skills, abilities of the applicant. At GitHub certain roles are eligible for benefits and additional rewards, including annual bonus and stock. These rewards are allocated based on individual impact in role. In addition, certain roles also have the opportunity to earn sales incentives based on revenue or utilization, depending on the terms of the plan and the employee's role.
This position will be open for a minimum of 3 days, with applications accepted on an ongoing basis until the position is filled.
GitHub Leadership Principles:
GitHub values
- Customer\-obsessed
- Ship to learn
- Growth mindset
- Own the outcome
- Better together
- Diverse and inclusive
Manager fundamentals
- Model
- Coach
- Care
Leadership principles
- Create clarity
- Generate energy
- Deliver success
Who We Are: GitHub is the world’s leading AI\-powered developer platform with 150 million developers and counting. We’re also home to the biggest open\-source community on earth (and 99% of the world’s software has open\-source code in its DNA). Many of the apps and programs you use every day are built on GitHub.
Our teams are dreamers, doers, and pioneers, leading the way in AI, driving humanitarian efforts around the globe, and even sending open source to Mars (and beyond!). At GitHub, our goal is to create the space you need to do your best work. We’re remote\-first and offer competitive pay, generous learning and growth opportunities, and excellent benefits to support you, wherever you are—because we know that people flourish when they can work on their own terms.
Join us, and let’s change the world, together.
EEO Statement: GitHub is made up of people from a wide variety of backgrounds and lifestyles. We embrace diversity and invite applications from people of all walks of life. We don't discriminate against employees or applicants based on gender identity or expression, sexual orientation, race, religion, age, national origin, citizenship, disability, pregnancy status, veteran status, or any other differences. Also, if you have a disability, please let us know if there's any way we can make the interview process better for you; we're happy to accommodate!
Salary Context
This $140K-$372K range is above the 75th percentile 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 GitHub, 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($256K) sits 8% below the category median. Disclosed range: $140K to $372K.
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.
GitHub AI Hiring
GitHub has 3 open AI roles right now. They're hiring across AI Software Engineer, Research Engineer. Based in Remote, US. Compensation range: $329K - $372K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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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