Staff Software Engineer, AV HIL Platforms

Talentify; Sunnyvale (CA); Hybrid; USD 189,000 - 290,000; Full time

Benefits offered by this job

Health benefits

Retirement plan

Paid vacation & holidays

Job summary

General Motors is seeking a Staff Software Engineer for the AV HIL Software Platforms Team in the GM Autonomous Vehicle organization. You will develop hardware-in-loop validation platforms, integrate with AI models, and work across AV Infrastructure, Embodied AI, Platform, and Vehicle Integration teams.

You’ll lead complex technical initiatives, mentor engineers, and help shape engineering standards, deployment pipelines, and observability.

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, or a related field; Master’s or PhD preferred.
• Strong coding ability in Python, C++, or comparable languages.
• Experience designing for scalability, performance, reliability, observability, and operational excellence.

Responsibilities

• Define and drive the long-term technical strategy, architecture, and roadmap for AV HIL software platforms and integration services across products and teams.
• Lead the design and evolution of scalable, reliable, observable, hardware-aware HIL capabilities for AV software validation.
• Own complex technical problems spanning AV software, AI models, vehicle hardware, middleware, OS, and deployment pipelines.

Skills

Python

C++

Linux

CI/CD

Distributed systems

Software architecture

Mentoring

Education

Bachelor's in CS/CE or related field

Master's or PhD preferred

Tools

ROS

Bazel

Docker

Kubernetes

Job description

General Motors is seeking a Staff Software Engineer for the AV HIL Software Platforms Team in the GM Autonomous Vehicle organization. You will develop hardware-in-loop validation platforms, integrate with AI models, and work across AV Infrastructure, Embodied AI, Platform, and Vehicle Integration teams.

You’ll lead complex technical initiatives, mentor engineers, and help shape engineering standards, deployment pipelines, and observability.