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About This Role
Job Description
The Role
The AI Engineering and Productivity team in the Global Planning, Design, and Product IT org is responsible for empowering Engineering and Quality teams by delivering reliable and secure AI-driven tools that streamline workflows, accelerate decision making, surface actionable insights, and unlock measurable productivity gains across the product lifecycle.
As a Senior Software Engineer , you will be responsible for developing data intensive and AI-enabled software applications that support Product Development engineering teams. You will work with complex data requirements and deliver solutions using industry standard practices across SQL, Databricks, and modern programming languages such as Java and Python. You will design and build software and data solutions based on detailed requirements and system specifications, with a strong focus on data quality, performance, and reliability.
This role offers the opportunity to advise, coach, and mentor other software engineers while shaping how engineering teams access and use data and AI. You will help define patterns and best practices for SQL, Databricks (DBX), and AI/ML integrations within VEQ and play a key role in ongoing modernization and cloud initiatives.
What You'll Do
- Design, develop, and maintain data driven and AI-enabled applications and services that support Product Development engineering teams.
- Write high-quality, performant SQL (queries, functions, stored procedures) for complex data transformations and modeling across enterprise data platforms (e.g., SQL Server, Oracle, PostgreSQL).
- Build and optimize data pipelines and workflows in Databricks (DBX) and related tools to support batch and near realtime data processing.
- Develop backend services and APIs in Java and/or Python that integrate data, business rules, and user workflows into robust, reusable components.
- Develop enterprise grade applications using Kubernetes/Docker, Quarkus, Java, Angular, PostgreSQL, and other GM approved tools.
- Partner with data science and AI teams to productionize AI/ML and LLM based solutions, including feature pipelines, inference integrations, monitoring, and continuous improvement.
- Proactively identify and remediate issues related to code quality, patterns, performance, security, and data correctness, using code quality analysis tools and remediation techniques.
- Lead or contribute to solution design, including architecture, patterns, and technology choices aligned with GM standards and Statement of Technical Direction.
- Apply and champion software engineering best practices, including code reviews, automated testing, branching strategies, CI/CD pipelines (e.g., GitHub Actions), observability, and secure coding practices.
- Collaborate with cross-functional teams (product owners, data engineers, architects, business stakeholders) to refine requirements, define acceptance criteria, and deliver incremental value in an Agile/SCRUM environment.
- Troubleshoot and resolve production issues across the full stack (data, application, infrastructure), driving root cause analysis, stable fixes, and clear documentation.
- Navigate complex corporate processes to remove project roadblocks and keep delivery on track.
- Provide guidance and mentoring to fellow software engineers and team members, helping grow skills in SQL, Databricks, cloud, and AI related technologies.
Document software and data solutions and ensure technical documentation meets GM standards and can be leveraged across VEQ and PD Business Systems. *
Your Skills & Abilities (Required Qualifications)
- Bachelor’s Degree in Computer Science, Software Engineering, Information Systems, Engineering, or a related field, OR equivalent experience.
- 6+ years of experience delivering enterprise or full stack software solutions using Java / JEE, Python , and preferably Angular .
- 3+ years of experience working with complex SQL queries, functions, and stored procedures, including performance tuning and optimization against large datasets.
- Experience building or supporting data pipelines , ETL/ELT processes, or datacentric applications on distributed or cloud platforms (e.g., Databricks, Spark , or similar).
- 3+ years of experience with Kubernetes/Docker, Quarkus , and cloud platforms such as Azure, AWS , or GCP .
- Experience working in Agile/SCRUM development methodologies, including backlog refinement, sprint planning, and incremental delivery.
- Hands on experience with modern DevOps practices such as Git/GitHub, code reviews, automated builds, automated testing, and CI/CD pipelines (e.g., GitHub Actions).
- Willingness and demonstrated ability to learn and apply AI concepts , including working with data and APIs that support AI/ML and LLM based solutions.
- Strong problem solving skills with the ability to break down complex technical and data challenges into clear, actionable steps and deliver high quality solutions.
- Excellent written and verbal communication skills with the ability to collaborate with both technical and nontechnical stakeholders.
Demonstrated ownership mindset, accountability for quality, and focus on delivering measurable value to internal customers. *
What Will Give You A Competitive Edge (Preferred Qualifications)
- Master’s Degree in Computer Science, Engineering, Information Systems, or a related field.
- 10+ years of experience delivering enterprise grade or global, scalable software applications , including data intensive or analytics focused solutions.
- Deep hands on experience with Databricks (Delta tables, notebooks, jobs, workflows) and/or Spark for data engineering, analytics, or AI workloads.
- Proven experience migrating applications and complex data workloads to Azure or other cloud platforms, including use of managed data, messaging, and API services.
- Experience working with or supporting AI/ML initiatives (e.g., feature engineering, model integration, AI/LLM service integration, monitoring, and ML Ops concepts).
- Knowledge of relational and dimensional data modeling, data quality practices, metadata management, and data governance in an enterprise environment.
- Experience with observability and reliability practices (logging, metrics, tracing, dashboards, alerting, SRE concepts) for data and application services.
- Demonstrated ability to influence technical direction, establish reusable patterns and standards, and mentor less experienced engineers.
- Ability to manage multiple initiatives and priorities in a fast-paced environment while maintaining high engineering standards.


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).

This role is categorized as hybrid. This means the selected candidate is expected to report to a specific location at least 3 times a week {or other frequency dictated by their 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.
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.
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
AI roles in Austin pay a median of $209,100 across 306 tracked positions.
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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