Software Engineer, AV Data Collection

Job Description

About the Team

The AV Logging and Data Recording team within GM’s Autonomous Vehicle Platform Core organization builds the embedded vehicle software that turns real-world driving into high-quality training data for autonomous driving.

Our systems collect the sensor, vehicle, and scenario data that AV teams use to improve perception, prediction, planning, and other artificial intelligence capabilities. We focus on capturing the right data from the fleet, preserving privacy, maintaining data quality, and reliably moving vehicle data into the AI development pipeline.

We build the software that helps autonomous vehicles learn from the real world.

About the Role

As a Software Engineer on the AV Data Recording team, you will help build embedded vehicle software that enables fleet-scale data collection for artificial intelligence development. Your work will support how GM autonomous vehicles capture meaningful real-world driving data, validate its quality, enforce privacy requirements, and deliver it for model training, evaluation, debugging, and continuous improvement.

You will work on software running on automotive edge platforms and collaborate closely with artificial intelligence, backend, embedded platform, and system development engineering teams to turn real-world driving into high-quality data for autonomous driving development.

This is an early-career / new graduate role designed for candidates who have recently completed, or will complete, their Bachelor’s, Master’s, or PhD degree before joining GM. You will be paired with experienced engineers to ramp up on AV software, embedded systems, real-time operating systems, vehicle data pipelines, and the production engineering practices required to support autonomous driving at fleet scale.

What You’ll Be Doing

• Develop embedded vehicle software that collects high-value data for autonomous driving AI learning.
• Build components that capture, filter, validate, package, and offload vehicle and sensor data from the fleet.
• Improve data quality, privacy enforcement, reliability, observability, and performance across vehicle data collection workflows.
• Build embedded software that controls how high-value driving data is captured, monitored, and delivered from vehicle compute platforms.
• Debug complex issues across Linux and QNX-based vehicle software, real-time data recording pipelines, and backend integration points.
• Collaborate with AI, backend, embedded platform, and system development engineering teams to ensure collected data meets autonomous driving development needs.
• Deliver reliable software through clear requirements, code reviews, automated testing, CI, issue tracking, and incremental development.
• Learn and apply production engineering practices for performance testing, regression prevention, rollout readiness, and operational support.

Basic Qualifications

• Recently completed, or currently pursuing, a Bachelor’s, Master’s, or Ph.D. degree in Computer Science, Computer Engineering, Electrical Engineering, Robotics, Artificial Intelligence, Machine Learning, or a related technical field. For completed degrees, graduation must have occurred within the past nine months.
• Degree must be completed prior to joining GM.
• Strong software fundamentals, including data structures, algorithms, operating systems, computer architecture, or distributed systems concepts.
• Solid coding ability in C++, Python, or both, demonstrated through coursework, internships, research, or substantial projects.
• Experience developing in a Linux environment through class projects, labs, internships, research, or personal projects.
• Ability to debug complex software problems, reason about system behavior, and communicate clearly in a collaborative team environment.
• Interest in autonomous vehicles, robotics, embedded systems, real-time operating systems, data platforms, artificial intelligence data pipelines, or high-performance software.

Preferred Qualifications

• Experience with embedded Linux, QNX, automotive systems, robotics platforms, or edge compute devices.
• Exposure to C++ systems programming, IPC, messaging, networking, serialization, or data pipelines.
• Experience with performance-sensitive, real-time, or resource-constrained software.
• Familiarity with logging, telemetry, observability, data quality, or data recording systems.
• Experience with build and test systems such as Bazel, CMake, GitHub Actions, or similar tooling.
• Exposure to SQL, cloud storage, backend services, or data processing workflows.
• Prior internship, research, or project experience in autonomous driving, robotics, embedded systems, distributed systems, AI/ML infrastructure, or platform software.

• Sunnyvale, CA
• 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.
• This job may be eligible for relocation benefits

• The salary range for this role: is $119,250 to $153,900. The actual base salary a successful candidate will be offered within this range 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.

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

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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.

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