Interested in this AI/ML Engineer role at General Motors (GM)?
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About This Role
Job Description
The Role:
Conduct original research and develop practical AI/ML methods that accelerate GM’s research, engineering, and product development capabilities. This role is intended for a recent or soon-to-be-completed Ph.D. who brings strong depth in one or more advanced AI areas and the ability to work across disciplines. The researcher will develop proof-of-concepts (POCs), evaluate them on meaningful engineering or scientific problems, communicate findings clearly, and collaborate with senior researchers and engineering partners to move promising ideas toward impact.
The Team:
GM R&D is seeking an outstanding early-career researcher to contribute to advanced work in Artificial Intelligence, Machine Learning, and Large Language Models. The role is designed for a fresh Ph.D. It emphasizes research depth, originality, experimental rigor, and growth toward independent technical leadership. The successful candidate will work with senior researchers, technical fellows, and engineering collaborators to formulate important problems, develop new methods, build research prototypes, and validate results. The work may span causal reasoning, neurosymbolic AI, world models, multimodal and generative AI, engineering agents, or related areas. The researcher will be expected to connect theory and experimentation with real engineering needs while maintaining high standards for reproducibility, explainability, uncertainty awareness, and responsible AI.
What You’ll Do:
- Conduct original research in one or more advanced AI areas such as causal AI, neurosymbolic AI, world models, multimodal or generative AI or engineering agents.
- Translate engineering and scientific challenges into well-defined research questions, hypotheses, algorithms, and experimental plans.
- Design, implement, and evaluate machine learning models and proof-of-concepts using real, simulated, experimental, or engineering data.
- Develop methods that can reason about physical systems, causal relationships, uncertainty, constraints, temporal evolution, or complex interactions among components.
- Establish meaningful baselines and evaluation protocols; analyze failure modes, robustness, generalization, interpretability, and uncertainty rather than relying only on headline accuracy.
- Build reproducible research code, data-processing workflows, and experiment pipelines using modern deep learning practices.
- Collaborate with GM R&D researchers and engineering partners to understand domain constraints, validate research results, and identify pathways from POCs to practical use.
- Communicate technical findings through research reports, presentations, publications, invention disclosures, demonstrations, and clear documentation.
- Contribute to technical discussions and research roadmaps by identifying promising approaches, important risks, and high-value experiments.
- Participate in a collaborative research culture that values intellectual curiosity, constructive peer review, responsible innovation, and continuous learning.
More About Who You Are
- A curious and intellectually rigorous researcher who enjoys working on difficult, open-ended problems.
- Deeply grounded in at least one technical area, while eager to learn and collaborate across adjacent AI, computational science, and engineering disciplines.
- Motivated by research that can create measurable value in real-world engineering workflows, not only benchmark performance.
- Comfortable moving between mathematical formulation, algorithm development, software implementation, and empirical validation.
- Able to explain complex ideas, assumptions, limitations, and results to both technical and general audiences.
- Independent in driving research tasks to completion while actively seeking feedback and working effectively with senior mentors and cross-functional teams.
- Committed to careful experimentation, reproducibility, safety, explainability, and responsible use of AI.
Required Qualifications:
- Pursuit or Completion of a PhD in one of the following areas: Computer Science, Artificial Intelligence, Machine Learning, Applied Mathematics, Statistics, Computational Science, Engineering, Physics, or a related field
- Able to work fulltime, 40 hours per week
- Demonstrated research depth in at least one relevant area, supported by a dissertation, publications, patents, open-source work, research software, or comparable technical contributions.
- Strong foundations in machine learning and deep learning, with working knowledge of optimization, probability/statistics, numerical methods, or scientific computing as appropriate to the research area.
- Experience developing and evaluating models in Python and frameworks such as PyTorch, JAX, TensorFlow, or comparable tools.
- Ability to formulate experiments, select appropriate metrics and baselines, analyze results, and communicate technical conclusions with appropriate caveats.
- Strong verbal and written communication skills and the ability to collaborate effectively with researchers, engineers, and technical stakeholders.
Ability to work on-site or in a hybrid research environment in accordance with GM work practices and project needs. *
Preferred Qualifications):
- Research experience in causal inference, counterfactual reasoning, neurosymbolic methods and world models for engineering applications.
- Experience with multimodal AI, large language models, agentic systems, retrieval and reasoning, or AI-assisted engineering and software development.
- Evidence of research impact through high-quality publications, patents, invention disclosures, open-source contributions, awards, or successful technology demonstrations.
Research or internship experience in automotive, mobility, aerospace, industrial, energy, defense, national laboratory, or university-industry collaboration settings. *
GM does not provide immigration-related sponsorship for this role. Do not apply for this role if you will need GM immigration sponsorship now or in the future. This includes direct company sponsorship, entry of GM as the immigration employer of record on a government form, and any work authorization requiring a written submission or other immigration support from the company (e.g., H1-B, OPT, STEM OPT, CPT, TN, J-1, etc).
The position is subject to export control restrictions and requires the successful candidate to be a U.S. Person (U.S. citizen, U.S. permanent resident, asylee or refugee).
Work Arrangement:
Hybrid: This role is categorized as hybrid. This means the successful candidate is expected to report to Mountain View, CA three times per week, at minimum [or other frequency dictated by the business].
To help facilitate administration of relocation benefits if you are selected, please apply using the permanent address you would move from.
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 actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position, as well as geography of the selected candidate.
The salary range for this role is $128,000-$279,000. 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.
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, life insurance, paid vacation and holidays, tuition assistance programs, employee assistance program, and GM vehicle discounts, among others.




 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 $128K-$279K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $111,000. Disclosed range: $128K to $279K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
General Motors (GM) AI Hiring
General Motors (GM) has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Research Engineer, Data Scientist. Positions span Warren, MI, US, Milford, MI, US, Mountain View, CA, US. Compensation range: $127K - $290K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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