AI Systems Engineer - Computation

GM•Warren, MI

1d•Onsite

About The Position

At General Motors, our product teams are redefining mobility. Through a human-centered design process, we create vehicles and experiences that are designed not just to be seen, but to be felt. We’re turning today’s impossible into tomorrow’s standard —from breakthrough hardware and battery systems to intuitive design, intelligent software, and next-generation safety and entertainment features. Every day, our products move millions of people as we aim to make driving safer, smarter, and more connected, shaping the future of transportation on a global scale. The Role We are seeking a Senior Engineer, AI Systems Engineering - Computation to join our Manufacturing Technology Development team within the General Motors Research and Development organization. In this role, you will innovate with robotics computation platforms, from digital twin simulations to edge-GPU architectures to hybrid cloud-edge orchestration. You will research new scheduling, resource allocation, and parallelization methods for training large AI workloads. NVIDIA-based s imulation s will be fundamental to how you scale training pipelines and with Robotics Intelligence .

Requirements

5 + years of experience in robotics computation, AI infrastructure, simulation, or embedded systems.
Strong background in GPU architectures, parallel computing, and cloud-edge orchestration.
Experience with real-time systems and AI deployment in dynamic environments.
Proven ability to collaborate across multidisciplinary teams.
Bachelor’s degree in Computer Science , Electrical Engineering, Robotics, or related field.

Nice To Haves

Master’s degree or PhD in AI, Robotics, or related field.
Experience with edge computing and embedded AI systems.
Track record of innovation including patents or publications.
Experience scaling AI systems from prototype to production.
Knowledge of cybersecurity and safety standards.

Responsibilities

Design and implement advanced robotics computation platforms including edge-GPU and hybrid cloud-edge systems.
Research and develop novel scheduling and resource allocation strategies for AI-intensive workloads.
Collaborate with AI & Simulation teams to scale training pipelines efficiently.
Enable real-time execution of novel algorithms in collaboration with Robotics Intelligence.
Ensure robustness, scalability, and efficiency of deployed computation systems.
Contribute to infrastructure innovation for AI systems integration.