Embedded AV Data Collection Engineer

General Motors Ventures; Sunnyvale (CA); Hybrid; USD 119,000 - 154,000; Full time

Job summary

General Motors Ventures is seeking an early-career Software Engineer to build embedded vehicle software for fleet-scale data collection on automotive edge platforms. You will work with AI, backend, and embedded teams to ensure data quality, privacy, and readiness for model training and evaluation.

The role emphasizes real-time data pipelines, Linux/QNX environments, and production engineering practices to support autonomous driving at scale.

Qualifications

• Strong software fundamentals including data structures, algorithms, operating systems, computer architecture, or distributed systems concepts.
• Solid coding ability in C++ and Python as demonstrated through coursework, internships, research, or projects.
• Experience developing in a Linux environment; ability to debug complex software problems and communicate in a team.

Responsibilities

• Develop embedded vehicle software that collects high-value data for autonomous driving AI learning.
• Build components to capture, filter, validate, package, and offload vehicle and sensor data.
• Improve data quality, privacy enforcement, reliability, observability, and performance of data collection workflows.
• Collaborate with AI, backend, embedded platform, and system engineering teams to meet autonomous driving data needs.
• Deliver reliable software through requirements, code reviews, automated testing, CI, and incremental development.

Skills

C++

Python

Data structures

Algorithms

Operating systems

Computer architecture

Distributed systems

Education

Bachelor's degree

Master's degree

PhD

Job description

General Motors Ventures is seeking an early-career Software Engineer to build embedded vehicle software for fleet-scale data collection on automotive edge platforms. You will work with AI, backend, and embedded teams to ensure data quality, privacy, and readiness for model training and evaluation.

The role emphasizes real-time data pipelines, Linux/QNX environments, and production engineering practices to support autonomous driving at scale.