General Motors
Software Engineering Manager (Autonomous Vehicle Data)
Austin, Texas, United States of America +1 · Hybrid · Manager · Full-time · Posted Oct 22, 2026
- Location
- Austin, Texas, United States of America
Sunnyvale, California, United States of America - Experience
- 8+ years in relevant software engineering, with 2+ years in formal engineering leadership.
- Education
- BA or BS in Computer Science, Electrical Engineering, Mathematics, Physics, or another relevant field; or equivalent real-world experience.
- Visa sponsorship
- GM does not provide immigration-related sponsorship for this role, now or in the future.
About the job
Leads a team building and operating petabyte-scale data pipelines and storage services that process autonomous-vehicle telemetry for downstream engineering and AI/ML workflows.
What you'll do
- Lead technical design and delivery while recruiting, mentoring, and growing the engineering team.
- Own the AV Data services vision and roadmap, including batch inference processing and streaming services for replay and simulation.
- Improve cloud efficiency, reliability, observability, and operational response across data services.
What they ask for
- BA or BS in a relevant field, or equivalent real-world experience.
- 8+ years of professional software engineering experience in distributed systems, big data platforms, or cloud infrastructure, including 2+ years in formal engineering leadership.
- Experience designing, operating, and scaling petabyte-scale distributed data pipelines and storage systems.
- Expert-level proficiency in C++, Rust, Go or Python, and expertise deploying and managing services on at least one major public cloud provider.
Nice to have
- Experience implementing data-platform services such as catalog, indexing, planning, pruning, and optimization at PB scale.
- Passion for self-driving technology; comfortable in a polyglot programming environment.
Tools
Apache Spark, Beam, Ray, Flink, Kafka, Iceberg, Delta Lake, Presto/Trino
AI focus
The platform supports perception, planning, ML training, safety metrics, simulation, and batch inference processing.
Model-written summary · the original posting is authoritative