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About This Role
Job Description
The Team:
The CoSim Infrastructure, Quality, and Scalability team, within GM’s Virtualization and SIL Integration organization, builds the foundational software, automation, and integration capabilities that make virtual ECUs and co-simulation environments reliable, reusable, and scalable across vehicle domains.
The team develops and evolves CoSim/SIL infrastructure—including SoftECU and FMI-based capabilities, RestBus and SIMETH connectivity, HWIO and calibration enablement, build and CI/CD pipelines, and automated quality gates—while improving package consistency, test coverage, observability, and defect triage.
Their work enables engineering, calibration, and verification teams to validate embedded-platform software earlier and with greater confidence, supporting daily regression testing as well as complex, multi-variant, multi-domain virtual validation. Within the broader Virtualization and SIL Integration organization, the team collaborates closely with domain teams to deliver high-fidelity virtual environments and advance GM’s software-defined vehicle development process.
The Role:
As a Staff Software Engineer , you will define the architecture for GM’s next‑generation virtual development platform, including SIL orchestration, AI‑enhanced simulation, data flows, and cloud‑native compute.
You’ll build on your strengths in automation, embedded systems, and debugging while expanding into cloud orchestration, IaC, ML workflows, and distributed simulation systems.
What You’ll Do (Responsibilities):
- Architect distributed simulation systems for vECUs, FMUs, and SIL/MIL workloads, ensuring scalable execution across heterogeneous environments.
- Define AI ‑ enabled simulation patterns including scenario generation, model‑based inference, and data pipelines.
- Design cloud ‑ native compute platforms optimized for large‑scale parallel, and elastically scalable simulation workloads.
- Establish API and service architecture for orchestration and data management.
- Guide CI/CD integration for simulation and AI components.
- Mentor developers and influence technical direction across teams.
Ensure security, observability, and reliability across the platform.
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Your Skills & Abilities (Required Qualifications):
- Bachelor’s degree (or higher) in Engineering, Computer Science, or related field.
- 10+ years of relevant experience in software architecture, simulation, or distributed systems.
- Expertise in backend and distributed systems.
- Experience with virtualization and simulation (vECUs, FMUs, SIL).
- Strong cloud architecture skills.
- Experience with CLI-based architecture and MCP-based architecture for AI tools design.
- Experience with ontology-based system design and architecture.
- AI/ML integration experience for simulation or data workflows.
- Knowledge of data engineering and storage patterns.
Experience with Kubernetes and container orchestration.
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What Will Give You a Competitive Edge (Preferred Qualifications):
- Experience designing simulation platforms at enterprise scale.
- Knowledge of automotive domain modeling and ontology.
- Experience with event ‑ driven architectures for simulation workflows.
- Experience with Agentic AI architecture and multi-agent coordination.
- Advanced knowledge of ontology technologies and ontology design patterns (Owl, RDF, SPARQL, Automated reasoning)
Advanced ML workflow experience (feature stores, model serving, vector DBs).
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Company Vehicle
Upon successful completion of a motor vehicle report review, you will be eligible to participate in a company vehicle evaluation program, though 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.
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 rang e for this role is $189,300 and $290,700. 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, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more.
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This role is based remotely, but if the selected candidate lives within a specific mile radius of a GM hub, they will be expected to report to the location three times a week {or other frequency dictated by your manager}.

Relocation benefits are available for candidates who qualify under company policy.

 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 $189K-$290K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $192K across 198 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 9,079 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At General Motors (GM), 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 $220,000 based on 1,601 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($240K) sits 9% above the category median. Disclosed range: $189K to $290K.
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 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 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).
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 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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