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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 is seeking a Principal Software Engineer to shape the future of identity and enterprise AI governance across its platform.
Identity is a critical foundation for GitHub’s agentic AI ecosystem, enabling secure interactions between users, applications, and AI agents. As AI adoption scales, systems must support trust, authorization, delegation, and policy enforcement at a global scale.
In this role, you will lead the architecture of Tier\-0 identity and governance services powering authentication, authorization, and policy enforcement for GitHub’s ecosystem. These systems must operate with the highest standards of security, reliability, and scalability, serving billions of requests daily.
You will partner across Security, Platform, AI, and Product teams to define strategy, drive architecture, and build the identity and governance control plane for GitHub’s AI\-powered future.
Responsibilities:
- Lead and influence design discussions for the overall system architecture of complex products and solutions, ensuring they meet security and compliance requirements.
- Establish and mentor others in best practices for testing and assuring the quality of solutions, defining success metrics, and producing maintainable code integrated with downstream dependencies.
- Provide technical leadership during code reviews to ensure solutions meet quality standards, are reliable, and are appropriate for the scale of the product feature.
- Partner with stakeholders such as project managers and technical leads to determine requirements for services or complex scenarios, leveraging feedback channels to incorporate insights into future designs.
- Act as an expert on debugging tools and methods, leading proactive and reactive code development to verify assumptions and resolve issues across products and teams.
- Optimize deployments across products to meet business objectives, ensuring solutions are deployed safely and adhere to GitHub's deployment standards.
- Lead efforts to improve development quality and team performance by driving the execution of strategies for developer tooling and automation, sharing best practices, and mentoring others in new tools and strategies.
- Lead the refinement of products through complex data analytics, making informed decisions to enhance engineering products and integrate data\-driven insights into the development process.
- Develop and implement strategies for identifying and mitigating technical risks across projects and products. This includes establishing frameworks for assessing potential risks and creating contingency plans to address them proactively.
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:* Experience building or operating enterprise AI governance platforms, including policy systems, compliance frameworks, and control planes
- Experience designing and implementing identity systems such as authentication services, authorization frameworks, and identity federation
- Familiarity with identity protocols such as OAuth, OIDC, SAML, and SCIM
- Experience with agentic or AI\-driven architectures, including systems involving models, agents, or AI APIs
- Strong understanding of enterprise security, compliance, and risk management requirements
- Experience operating cloud\-native systems in environments such as Azure, Kubernetes, or AKS
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. 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: $189K across 518 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 26,159 AI roles we're tracking, AI Software Engineer positions make up 2% 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 $235,100 based on 665 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($292K) sits 24% above the category median. Disclosed range: $160K to $425K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
GitHub AI Hiring
GitHub has 5 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span Remote, US, US. Compensation range: $329K - $425K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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