Senior Software Engineer, AI Solutions

Responsibilities

• Remote: This role is based remotely but if you live within a 50-mile radius of [Atlanta, Austin, Detroit, Warren, Milford or Mountain View], you are expected to report to that location three times a week, at minimum
• The AI Solutions team in the GM AV Organization is responsible for end-to-end deployment of machine learningmodels for inference from frameworks (e.g. PyTorch) to the autonomous vehicle hardware platform, as well as the ML runtime software needed to execute those models on the vehicle
• This role blends expertise in ML systems, model serving, software optimization, operating systems, and hardware-aware engineering
• Deploy machine learning (ML) models that drive self-driving vehicles by leveraging ML compilers, hardware-aware ML optimizations, and ML runtimes, targeting both onboard (vehicle) and offboard (cloud/simulation) environments
• Develop low-level ML runtime software and APIs to efficiently serve and execute models in onboard and offboard environments
• Collaborate with engineers from Embodied AI, Kernels, Compilers, Architecture, and System Performance teams

Full Description

Remote: This role is based remotely but if you live within a 50-mile radius of [Atlanta, Austin, Detroit, Warren, Milford or Mountain View], you are expected to report to that location three times a week, at minimum.

The Role

The AI Solutions team in the GM AV Organization is responsible for end-to-end deployment of machine learningmodels for inference from frameworks (e.g. PyTorch) to the autonomous vehicle hardware platform, as well as the ML runtime software needed to execute those models on the vehicle. We work closely with partners in the Embodied AI org, as well as AI Kernels, AI Compilers, and AI Architecture. This role blends expertise in ML systems, model serving, software optimization, operating systems, and hardware-aware engineering.

What You’ll Do

• Deploy machine learning (ML) models that drive self-driving vehicles by leveraging ML compilers, hardware-aware ML optimizations, and ML runtimes, targeting both onboard (vehicle) and offboard (cloud/simulation) environments.

• Develop low-level ML runtime software and APIs to efficiently serve and execute models in onboard and offboard environments.

• Collaborate with engineers from Embodied AI, Kernels, Compilers, Architecture, and System Performance teams.