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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:
We are looking for a Principal Software Engineer to define and build the foundational systems for GitHub’s Enterprise AI Platform.
This role sits at the intersection of distributed systems, AI/agent platforms, developer tooling, identity, policy, and platform architecture. You will shape how AI agents operate safely and consistently across IDEs, CLI, MCP, long\-running agents, and future Copilot experiences.
This is a highly strategic, high\-ambiguity role with broad technical influence across Copilot, platform infrastructure, client experiences, identity, billing, and enterprise products.
You will define platform primitives that shape enterprise AI adoption across millions of developers and tens of thousands of organizations across GitHub’s ecosystem.
Responsibilities:
This is a systems architecture and platform definition role requiring deep technical ownership across multiple organizations.* Define Enterprise AI Control Plane Architecture: Drive the technical direction for a unified AI control plane spanning models, agents, tools, MCP, permissions, spend, plugins, and client settings with a vision of policy authored once, enforced consistently everywhere.
- Build Foundational Platform Primitives: Architect scalable policy engines, governance SDKs, client\-side enforcement frameworks, and extensible schemas for agentic workflows.
- Drive Cross\-Client Consistency: Partner across Copilot CLI, IDE, agent, and platform teams to standardize policy enforcement through reusable APIs, contracts, and SDK patterns.
- Solve Hard Distributed Systems Problems at Scale: Design reliable, scalable, secure, and resilient systems for policy evaluation, spend enforcement, permissions, auditability, and observability supporting millions of developers.
- Influence GitHub’s AI Platform Strategy: Partner with engineering and product leaders to shape the long\-term enterprise AI roadmap, technical direction and cross\-organizational alignment.
- Raise the Engineering Bar: Mentor senior engineers and elevate architectural rigor, systems thinking, and technical execution across teams.
Qualifications:
Required Qualifications* 11\+ years experience in Software Engineering, Computer Science, or related technical discipline with proven experience maintaining and delivering production software coding in languages including, but not limited to, C, C\+\+, C\#, Java, JavaScript, Go, Ruby, Rust, or Python,
+ OR Associate’s Degree in Computer Science, Electrical Engineering, Electronics Engineering, Math, Physics, Computer Engineering, Computer Science, or related field AND 10\+ years experience in Software Engineering, Computer Science, or related technical discipline with proven experience maintaining and delivering production software coding in languages including, but not limited to, C, C\+\+, C\#, Java, JavaScript, Go, Ruby, Rust, or Python,
+ OR Bachelor's Degree in Computer Science or related field AND 9\+ years experience in Software Engineering, Computer Science, or related technical discipline with proven experience maintaining and delivering production software coding in languages including, but not limited to, C, C\+\+, C\#, Java, JavaScript, Go, Ruby, Rust, or Python,
+ OR Master's Degree in Computer Science, Electrical Engineering, Electronics Engineering, Math, Physics, Computer Engineering, Computer Science, or related field AND 7\+ years experience in Software Engineering, Computer Science, or related technical discipline with proven experience maintaining and delivering production software coding in languages including, but not limited to, C, C\+\+, C\#, Java, JavaScript, Go, Ruby, Rust, or Python,
+ OR PhD Degree in Computer Science, Electrical Engineering, Electronics Engineering, Math, Physics, Computer Engineering, Computer Science, or related field AND 5\+ years experience in Software Engineering, Computer Science, or related technical discipline with proven experience maintaining and delivering production software coding in languages including, but not limited to, C, C\+\+, C\#, Java, JavaScript, Go, Ruby, Rust, or Python,
+ OR equivalent experience.
Preferred Qualifications:* Deep Distributed Systems Expertise: Deep experience building and operating large\-scale distributed systems or platform infrastructure.
- Platform \& Systems Design: Strong expertise designing scalable systems across APIs, identity, policy engines, security, reliability, and infrastructure platforms.
- AI / Platform Experience: Proven experience building platform infrastructure, developer platforms, AI systems, or large\-scale technical platforms.
- Cross\-Organizational Technical Leadership: Demonstrated success driving architecture, platform adoption, and alignment across multiple teams.
- Influence Without Authority: Strong track record of shaping technical direction and delivering outcomes across organizational boundaries.
- Builder Mindset: Comfortable operating in ambiguity and fast\-moving environments, creating clarity, and balancing long\-term platform investments with pragmatic execution.
- Preferred Experience: Background in AI/LLM systems, agentic platforms, developer tooling, governance systems, enterprise software, or cloud\-native systems (Azure, Kubernetes, AKS) is a plus.
Compensation Range: The base salary range for this job is USD $160,200\.00 \- USD $425,000\.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 $160K-$425K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $190K across 251 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 4,133 AI roles we're tracking, AI Software Engineer positions make up 8% of the market. At GitHub, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $232,000 based on 863 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($292K) sits 26% above the category median. Disclosed range: $160K to $425K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
GitHub AI Hiring
GitHub has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Remote, US. Compensation range: $425K - $425K.
Remote Work Context
Remote AI roles pay a median of $173,300 across 2,012 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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