Interested in this AI/ML Engineer role at GitHub?
Apply Now →Skills & Technologies
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's mission is to accelerate human progress through developer collaboration. GitHub Copilot is redefining how software gets built and our AI GTM team (Copilot Specialists) ensures that the world's most innovative companies can realize its full value.
The AI GTM team is a high\-impact group that partners with GitHub Sales, Microsoft (MCAPS), Product, Enablement, Customer Success and Marketing teams to accelerate Copilot adoption. Our goal: land strategic wins, scale best\-in\-class sales activation, and help developers everywhere build better software with AI.
The Strategic AI GTM Lead, Americas is an individual contributor who acts as the tip of the spear for our most important opportunities. Supporting Enterprise segments, you will serve as a trusted advisor and technical partner in high\-impact, revenue\-generating customer engagements including pre\-sales discovery, competitive takeouts (GitLab, Bitbucket), Cursor/AI IDE displacement, and strategic expansions. This role combines deep product fluency, sales execution, and industry insight. You will bring AI\-native credibility, technical depth, and customer empathy to every conversation. Think of it as special forces for GTM: highly trained, highly capable, and deployed with precision to move the needle where it matters most.
Why this role? This is a rare opportunity to operate at the cutting edge of developer productivity and AI transformation. As a Strategic AI GTM Lead, Americas, you won't just talk about the future of software \- you will help build it.
Responsibilities:
Support Customer Engagements
- Partner with GitHub, Microsoft, and Channel sellers to influence and close complex Copilot deals in your coverage segment (Enterprise).
- Lead strategic customer engagements, from technical discovery through proof\-of\-value and close.
- Identify and engage with customer technical decision makers and influencers while engaging the sales team and helping to execute sales strategy.
Leverage and Support Partner Ecosystem
- Support competitive engagements, such as AI competitor discussions or integration\-platform positioning.
- Engage in partner sell\-with scenarios by acting as liaison between the partner and team and facilitating partner resources and processes throughout the course of the project.
Build Strategy
- Translate product vision into compelling, customer\-aligned narratives grounded in developer value and ROI.
- Maintain deep awareness of the rapidly evolving AI coding space \- new entrants, use cases, and trends.
- Capture core competitive knowledge and deliver back to product and engineering teams to enhance team capabilities and develop compete strategies for assigned customers.
Solution Design and Proof
- Demonstrate and/or oversee demonstrations of products, services, and integration through initial engagements.
- Support development of differentiated offers with hands\-on keyboard work via proof of concept (POC).
Education
- Deliver industry thought leadership during customer briefings (EBC), events, and roadmap sessions.
- Synthesize field insights and customer needs into actionable product feedback.
- Build a readiness plan and proactively identify learning gaps to grow domain knowledge and expertise.
Accelerating Product Revenue (Sales)
- Drive pipeline and account prioritization strategies with Sales, Marketing, and RevOps teams.
- Collaborate with field enablement to scale best practices and sharpen our Copilot GTM motion.
- Partner with internal stakeholders at GitHub (EPD) and Microsoft (MCAPS) to identify strategies for new opportunities.
Qualifications:
Required/Minimum Qualifications
- 9\+ years experience in sales, pre\-sales, technical consulting, or related field
+ OR Bachelor's Degree in Business, Information Technology, or related field AND 7\+ years experience in sales, pre\-sales, technical consulting, or related field
+ OR equivalent experience.
- 5\+ years experience selling Developer Automation, Artificial Intelligence, CICD, Metered Developer Tools, DevOps, Application Security, or DevSecOps solutions.
- Ability to travel up to 25% when needed.
Additional or Preferred Qualifications
- 5\+ years experience supporting multiple reps/regions/geos as a product specialist. This includes forecasting, identifying opportunities/needs, and setting priorities across entire territory.
- 5\+ years experience selling or co\-selling cloud infrastructure such as Azure, AWS or GCP.
- 5\+ years experience developing, collaborating, executing on go\-to\-market strategies
- Familiarity with Microsoft co\-sell processes.
- Background in developer evangelism or solutions architecture.
Compensation Range: The base salary range for this job is USD $135,520\.00 \- USD $359,450\.00 /Yr.
In addition, this role also has the opportunity to earn sales incentives. On target earnings (OTE) is based on a 70/30 base salary/sales incentive.
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 $135K-$359K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At GitHub, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($247K) sits 48% above the category median. Disclosed range: $135K to $359K.
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/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.