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About This Role
Job Description
The Role
As a Lead AI Workflow Engineer – Enterprise Transformation, you will design, build, and scale AI\-enabled workflow and automation solutions that modernize how work gets done across General Motors. From the software and core IT side of the transformation, you will focus on eliminating manual effort, simplifying workflows, improving consistency, and enabling business teams to operate effectively across Google Workspace, enterprise platforms, and AI\-enabled environments.
This role is a strong fit for an experienced engineer who can independently deliver complex workflow solutions, translate business needs into practical technical designs, and build secure, reusable, and supportable automations that align with enterprise standards. You will partner closely with product, engineering, technical program management, security, and business stakeholders to turn high\-value manual processes into scalable solutions and to contribute reusable patterns that can be adopted broadly across GM.
What You’ll Do
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### AI workflow engineering and solution delivery
- Design, build, test, and support AI\-enabled workflow and automation solutions across collaboration platforms, enterprise systems, and cloud\-native environments.
- Develop secure backend services, scripts, integrations, and orchestration logic using Python and/or JavaScript frameworks such as Node.js and TypeScript.
- Build and support event\-driven services and workflow components using approved cloud\-native patterns and enterprise integration standards.
- Integrate workflow solutions with enterprise platforms such as IT service management, HR, CRM, document and content platforms, reporting systems, and internal API ecosystems.
- Translate manual, fragmented, or legacy processes into scalable, supportable, and reusable automation designs.
### Workflow transformation and business process modernization
- Partner with business stakeholders, transformation leads, and product teams to understand current\-state pain points and identify practical workflow improvement opportunities.
- Help redesign business processes before automation so teams do not simply recreate inefficient legacy ways of working in new platforms.
- Support modernization of Microsoft\-dependent, spreadsheet\-heavy, macro\-driven, or manually routed workflows into more durable Google\-aligned and cloud\-native solutions.
- Contribute to phased transition and coexistence models where legacy tools or downstream systems must temporarily remain in place during rollout.
### AI enablement and intelligent orchestration
- Build and support AI\-assisted workflow solutions using approved enterprise AI platforms and orchestration patterns (for example, Gemini\-based capabilities and internal AI tools).
- Implement prompt logic, tool orchestration, exception handling, and validation steps for AI\-enabled processes with a strong focus on reliability and supportability.
- Contribute to human\-in\-the\-loop workflow designs where business or compliance requirements require review, approval, or validation checkpoints.
- Implement logging, telemetry, and basic evaluation mechanisms to monitor workflow health, agent performance, and operational issues.
### Technical design, reuse, and standards alignment
- Evaluate incoming business requests and recommend the appropriate solution path using approved platform capabilities, configuration, low\-code/no\-code options, or custom engineering.
- Build reusable services, templates, reference implementations, and accelerators that help teams solve repeatable workflow problems more consistently.
- Apply enterprise platform standards, approved design patterns, and security requirements in all delivered solutions.
- Document workflow designs, technical logic, dependencies, integration points, and operational runbooks to support long\-term sustainment.
- Participate in peer reviews, design reviews, and technical discussions to maintain quality, consistency, and supportability.
### Cross\-functional delivery and sustainment
- Work closely with TPMs, product managers, security, privacy, identity, ILM, and business stakeholders to ensure solutions are practical, compliant, and supportable.
- Support pilot deployments, early\-wave implementations, validation activities, and hypercare for high\-priority transformation use cases.
- Troubleshoot production issues, resolve workflow failures, and improve resiliency across connected systems and downstream dependencies.
- Provide delivery feedback and practical recommendations that help improve future support models, governance processes, and reusable platform patterns.
Your Skills \& Abilities (Required Qualifications)
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### Core experience and education
- 7\+ years of hands\-on experience in workflow engineering, automation engineering, software engineering, platform engineering, or enterprise application automation within a complex, highly matrixed corporate environment.
- Proven experience analyzing, redesigning, and automating complex business processes that span multiple connected systems and stakeholder groups.
- Demonstrated success modernizing legacy or manual workflows into more scalable, API\-driven, event\-based, or automation\-enabled solutions.
- Experience supporting enterprise platform change, migration, modernization, or transformation initiatives in large organizations.
- Bachelor’s degree in Computer Science, Information Technology, Engineering, Information Systems, or a related technical field, or equivalent practical experience.
### Workflow engineering and integration capability
- Strong hands\-on experience building backend automations, workflow logic, integrations, and supportable technical solutions using Python and/or JavaScript\-based tooling.
- Experience working with REST APIs, webhooks, connectors, event\-based logic, and orchestration patterns across enterprise systems.
- Proven ability to build secure, reliable integrations across collaboration tools, SaaS platforms, and internal enterprise applications.
- Strong understanding of modern SaaS platforms, cloud\-based applications, and enterprise workflow architecture patterns.
### AI workflow and intelligent automation experience
- Practical experience integrating AI capabilities into business workflows, automation pipelines, or enterprise tools.
- Familiarity with prompt design, workflow orchestration, exception handling, validation logic, and supportable AI\-assisted process design.
- Experience implementing logging, telemetry, monitoring, or evaluation approaches for automated or AI\-enabled workflows in production or near\-production settings.
### Systems thinking, security, and delivery discipline
- Strong systems\-thinking skills with the ability to trace dependencies across applications, business processes, data layers, and identity boundaries.
- Solid understanding of authentication, authorization, access\-control concepts, and secure integration practices in enterprise environments.
- Experience using version control, structured testing, and release management practices, and debugging across multiple environments.
- Strong technical documentation skills, including the ability to clearly describe workflow logic, business rules, dependencies, and support processes.
- Strong cross\-functional communication skills with experience working closely with product teams, TPMs, security, and business stakeholders.
### What Will Give You a Competitive Edge (Preferred Qualifications)
- Experience with Google Workspace administration, Google Workspace APIs, Google Apps Script, AppSheet, or related Google ecosystem tooling.
- Experience modernizing or replacing legacy macros, spreadsheet\-heavy processes, low\-code flows, SharePoint or similar unmanaged automation patterns.
- Experience integrating AI platforms or intelligent workflow capabilities using tools such as Gemini, Glean, AskGM, Databricks, or similar enterprise platforms.
- Familiarity with cloud\-native workflow components, event\-driven design, and modern enterprise orchestration approaches.
- Experience supporting pilot deployments, testing, cutover planning, validation, hypercare, and sustainment in large transformation programs.
- Experience building reusable templates, shared automation components, or reference implementations that improve scale and consistency across teams.
- Google Cloud or Google Workspace\-related certifications.
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 $163k–$250k 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 $163K-$250K range is above the median 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. Disclosed range: $163K to $250K.
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
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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