General Motors
Sr. Software Engineer - AI Engineering and Productivity
Austin, Texas, United States of America +1 · Hybrid · Senior · Full-time · Posted Oct 8, 2026
- Location
- Austin, Texas, United States of America
Warren, Michigan, United States of America - Experience
- 6+ years in enterprise or full-stack software delivery; 3+ years with complex SQL; 3+ years with Kubernetes/Docker, Quarkus, and cloud platforms. Preferred: 10+ years delivering enterprise-grade or scalable applications.
- Education
- Bachelor’s degree in Computer Science, Software Engineering, Information Systems, Engineering, or a related field, or equivalent experience.
- Visa sponsorship
- GM does not provide immigration-related sponsorship for this role; applicants who need sponsorship now or in the future should not apply.
About the job
Builds data-intensive, AI-enabled software and data solutions that help Product Development engineering teams improve workflows and decision-making.
What you'll do
- Develop data-driven applications, data pipelines, and backend services using SQL, Databricks, Java, and/or Python.
- Partner with AI/ML teams to productionize AI and LLM solutions, and design reliable, secure, high-performance systems.
- Lead technical design and engineering practices, troubleshoot production issues, and mentor other engineers.
What they ask for
- Bachelor’s degree in a related field or equivalent experience.
- 6+ years delivering enterprise or full-stack software solutions using Java/JEE and Python; Angular is preferred.
- 3+ years writing and optimizing complex SQL, and 3+ years with Kubernetes/Docker, Quarkus, and a cloud platform such as Azure, AWS, or GCP.
- Experience with data pipelines or ETL/ELT, Agile/SCRUM, and modern DevOps practices including CI/CD; ability to learn and apply AI concepts.
Nice to have
- Master’s degree in a related field; 10+ years delivering enterprise-grade or scalable software applications.
- Hands-on Databricks or Spark experience, cloud migrations, AI/ML initiatives, data governance, observability, or mentoring and setting technical direction.
Tools
SQL, Databricks, Java, Python, Kubernetes/Docker, Quarkus, Angular, GitHub Actions
AI focus
Productionize AI/ML and LLM-based solutions, including feature pipelines, inference integrations, monitoring, and continuous improvement.
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