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About This Role
Job Description
The Role:
We are seeking an experienced, technically strong, impact\-driven expert in ML Training Infrastructure with a demonstrated ability to lead through hands\-on technical work. In this role, you will be responsible for defining the technical direction and driving the design and development of scalable, reliable, and high\-performance AI/ML platform infrastructure that enables advanced AI research and model development at scale.
As a Staff ML Engineer, you will operate as a technical leader across initiatives, partnering closely with machine learning engineers, research scientists, and platform teams to shape architecture, drive major technical decisions, and deliver state\-of\-the\-art AI infrastructure that enables the future of intelligent driving technologies across General Motors vehicles.
What You'll Do:
- Define and drive the architecture, design, and development of scalable, reliable, and high\-performance ML frameworks and platform capabilities to support model training at scale.
- Lead model training performance analysis and optimization efforts across distributed training workflows, improving scalability, efficiency, and cost across heterogeneous hardware environments.
- Raise the bar on system observability, debuggability, operational excellence, and developer experience across the ML training stack.
- Own large, ambiguous, cross\-functional technical initiatives from strategy through execution, including technical roadmap definition, tradeoff analysis, and delivery.
- Influence platform direction by identifying long\-term infrastructure investments, setting engineering standards, and driving adoption of best practices across teams.
- Collaborate across organizational boundaries to align requirements, resolve technical disagreements, and integrate new capabilities into the platform ecosystem.
- Mentor engineers through design reviews, technical guidance, and hands\-on partnership, while elevating engineering quality across the team.
Your Skills \& Abilities (Required Qualifications)
- Bachelor's degree or higher in Computer Science or a related field, or equivalent practical experience.
- 7\+ years of professional software engineering experience.
- 5\+ years of specialized experience in AI/ML infrastructure, such as enabling distributed training for large\-scale ML models.
- Strong programming skills in Python, with deep proficiency in frameworks such as PyTorch (preferred), TensorFlow, or similar ML systems.
- Proven experience designing and operating distributed systems for ML training, including distributed computing, GPU computing, and cloud environments (AWS, GCP, Azure).
- Demonstrated track record of leading technically ambiguous, cross\-team infrastructure initiatives and driving them to measurable impact.
- Strong architectural judgment and ability to make sound technical tradeoffs across performance, reliability, usability, and cost.
- Willingness to travel to Sunnyvale, CA as needed.
- Comfortable operating in highly ambiguous and dynamic environments.
What Will Give You a Competitive Edge (preferred qualifications):
- 7\+ years of professional software engineering experience.
- Deep expertise in PyTorch 2\.x\+ and distributed training frameworks.
- Experience designing and developing training platforms that support FSDP, pipeline parallelism, and other scalable solutions for training large foundational models.
- Experience profiling, analyzing, debugging, and optimizing training and data loading performance at scale.
- Strong record of technical leadership through architecture reviews, roadmap influence, and cross\-team execution.
- Excellent communication skills, with the ability to build consensus, navigate controversial decisions, communicate risks clearly, and provide constructive technical feedback.
- Self\-motivated, execution\-oriented, and motivated by delivering broad organizational impact.
Compensation: The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The compensation may not be representative for positions located outside of the California Bay Area.
- The salary range for this role is $185,000 to $335,300\. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.
- Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.
Relocation: This job may be eligible for relocation benefits.
Benefits:
- Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation \& holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more.
Company Vehicle : Upon successful completion of a motor vehicle report review, you will be eligible to participate in a company vehicle evaluation program, through which you will be assigned a General Motors vehicle to drive and evaluate. Note: program participants are required to purchase/lease a qualifying GM vehicle every four years unless one of a limited number of exceptions applies.
\#GM\-AV\-1
\&\#xa;\&\#xa;\&\#xa;\&\#xa;This role is categorized as remote. This means the selected candidate may be based anywhere in the country of work and is not expected to report to a GM worksite unless directed by their manager.\&\#xa;\&\#xa;This job may be eligible for relocation benefits.\&\#xa;\&\#xa;
About GM
Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.
Why Join Us
We believe we all must make a choice every day – individually and collectively – to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team.
Benefits Overview
From day one, we're looking out for your well\-being–at work and at home–so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources .
Non\-Discrimination and Equal Employment Opportunities (U.S.)
General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers.
All employment decisions are made on a non\-discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws.
We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role\-related assessment(s) and/or a pre\-employment screening prior to beginning employment. To learn more, visit How we Hire .
Accommodations
General Motors offers opportunities to all job seekers including individuals with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, email us or call us at 1\-800\-865\-7580\. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.
Salary Context
This $185K-$335K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At General Motors (GM), 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($260K) sits 45% above the category median. Disclosed range: $185K to $335K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
General Motors (GM) AI Hiring
General Motors (GM) has 11 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Research Scientist, Data Engineer. Positions span Sunnyvale, CA, US, Austin, TX, US, Warren, MI, US. Compensation range: $261K - $347K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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