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About This Role
Job Description
The Role
In this role, you will own the end\-to\-end transformation of business workflows using AI. Rather than just building prompts or standalone agents, you will design secure, reusable, enterprise\-grade AI solutions that modernize how work gets done across GM. You will partner closely with engineering, product, and business teams to define platform standards, shape architecture, and scale AI adoption, from intake and solution design through governance and long\-term sustainment. You will also work directly with business teams to identify high\-value workflow transformation opportunities and drive consistent AI patterns across both collaboration and enterprise AI platforms.
What You’ll Do
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- Write clean, scalable, and secure backend services and integrations using Python and/or JavaScript frameworks (Node.js, TypeScript).
- Build and operate cloud\-native, event\-driven services on GCP using Cloud Run, Pub/Sub, Cloud Functions, and API Gateway.
- Code data pipelines and integration layers to migrate legacy enterprise workflows (e.g., M365\) into native GCP environments.
- Implement robust security in integrations using GCP IAM, OAuth2, service accounts, and enterprise access controls.
- Build and maintain automated testing frameworks, CI/CD pipelines (GitHub), and structured release environments.
- Engineer and deploy production\-grade multi\-step AI agents and bot\-driven workflows integrated with Vertex AI, Gemini, and external LLMs.
- Architect and code Human\-in\-the\-Loop (HITL) validation nodes into automated pipelines for critical business processes.
- Design, test, and optimize production prompts and agent instructions with guardrails, exception handling, and failure\-recovery logic.
- Implement telemetry, structured logging, and evaluation frameworks to monitor agent performance, accuracy, and latency.
- Evaluate ambiguous business requests and author technical designs that translate them into clear execution pathways.
- Own technical intake decisions: recommend when to use platform capabilities, configuration, or custom AI solutions.
- Create reusable architecture patterns, code templates, and shared libraries that enable internal developers to scale AI workflows.
- Deliver comprehensive technical documentation, API specifications, blueprint repositories, and operational runbooks with all deployed code.
- Lead rigorous code reviews for peer engineering levels to maintain code quality, security, and architectural alignment.
Your Skills \& Abilities (Required Qualifications)
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- 10\+ years of hands\-on experience in software engineering, platform architecture, or enterprise workflow automation in complex, highly matrixed corporate environments.
- Proven experience leading or contributing to GCP\-based transformations, including cloud migration, modernization of enterprise workloads, and adoption of scalable, secure, AI\-enabled solutions.
- Strong track record navigating large\-scale technology transformations, including tenant\-to\-tenant migrations and platform shifts (e.g., migrating workflows out of Microsoft M365\).
- Demonstrated success modernizing legacy or manual workflows into API\-driven, event\-based, or agent\-assisted applications.
- Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or a related technical field, or equivalent practical experience.
- Experience designing and deploying AI agents or bot\-driven workflows using frameworks integrated with Vertex AI, Gemini, or external LLMs.
- Deep understanding of multi\-step workflow orchestration, including tool use, state handling, and integration across enterprise services.
- Practical experience building reliable, production\-grade prompts and agent instructions with strong guardrails and failure handling.
- Demonstrated experience implementing telemetry, logging, and evaluation frameworks for production AI agents.
- Strong hands\-on experience with GCP services such as Cloud Run, Pub/Sub, Cloud Functions, IAM, and API Gateway in event\-driven designs.
- Proven ability to integrate bots/agents with enterprise systems (ITSM, HR, CRM, internal APIs) using secure REST/webhook patterns.
- Deep understanding of GCP IAM, OAuth2, service accounts, and enterprise access controls in multi\-system integrations.
- Strong proficiency in Python and/or JavaScript frameworks (Node.js, TypeScript) building production\-ready backend services and integrations.
- Mastery of core software engineering guardrails, including GitHub\-based version control, automated testing, and structured release practices.
- Proven capability to evaluate ambiguous business requests and define clear execution paths (out\-of\-the\-box vs. custom engineering).
- Strong foundational knowledge of corporate identity and access management and corporate data security policies.
- History of delivering clean documentation, handover plans, and operational runbooks alongside deployed code.
- Exceptional communication skills with experience running code reviews, collaborating with TPMs, and leading technical workshops.
What Can Give You a Competitive Advantage (Preferred Qualifications)
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- Deep experience leveraging Vertex AI Agent Builder, LangChain, or LlamaIndex on GCP to build enterprise\-grade, multi\-agent systems.
- Proven success migrating legacy enterprise workflows (e.g., Microsoft M365, Power Automate, SharePoint) into native GCP architectures (Cloud Run, Pub/Sub, Vertex AI).
- Experience implementing advanced LLM evaluation frameworks (e.g., Vertex AI AutoSxS, Ragas) and security guardrails (e.g., NeMo Guardrails) for safe production use.
- Practical experience designing complex agentic workflows with HITL validation nodes for highly regulated business processes.
- Track record of building repeatable platform architecture patterns, shared code libraries, and boilerplate templates that enable federated business units to build AI workflows independently.
- Demonstrated success driving cultural and technical adoption of AI tools, including training frameworks and technical enablement workshops.
- Professional Google Cloud certifications such as Professional Cloud Architect, Professional Data Engineer, or Professional Machine Learning Engineer (PMLE).
This job may be eligible for relocation benefits.
GM DOES NOT PROVIDE IMMIGRATION\-RELATED SPONSORSHIP FOR THIS ROLE. DO NOT APPLY FOR THIS ROLE IF YOU WILL NEED GM IMMIGRATION SPONSORSHIP (e.g., H\-1B, TN, STEM OPT, etc.) NOW OR IN THE FUTURE.
For a California\-only role , use:
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.
Salary Range: The salary range for this role in California is $202k–$310k per year . The actual base salary offered 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.
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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 $202K-$310K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($256K) sits 19% above the category median. Disclosed range: $202K to $310K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
General Motors (GM) AI Hiring
General Motors (GM) has 13 open AI roles right now. They're hiring across Data Engineer, AI/ML Engineer, Data Scientist, AI Product Manager. Positions span Warren, MI, US, Austin, TX, US, Sunnyvale, CA, US. Compensation range: $173K - $335K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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