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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior AI/ML Performance Engineer - **Company:** General Motors - **Location:** Sunnyvale, CA, United States (Remote available) - **Experience:** Expert - **Salary:** $144,700.0 - $261,300.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Software Applications, Systems Engineering, Microsoft Azure, Big Data, BigQuery, Cloud Computing, Profiling, Software Debugging, Python (Programming Language), Machine Learning, Open Source Technology, Performance Tuning, Cloud Platform System, Pytorch, Grafana, Containerization, Kubernetes, Information Technology, HuggingFace, Data Analytics, Machine Learning Operations, Software Coding - **Published:** September 3, 2026 - **Apply:** https://www.themuse.com/jobs/generalmotors/senior-aiml-performance-engineer ## About the Role * Experience: 5+ years of professional experience in high-scale infrastructure or ML systems. * Education: Bachelor's Degree in Computer Science, a related technical field, or equivalent practical experience. * Software Proficiency: Expert-level coding skills in Python and the ability to architect/debug within the PyTorch ecosystem. * Systems Engineering: Proven track record of resolving performance issues within large-scale distributed production environments., * Containerization: Hands-on experience with Kubernetes for orchestrating complex workloads. * GPU Monitoring: Technical proficiency with Nvidia DCGM , nvidia-smi , and Grafana for real-time telemetry and observability. * Cloud Platforms: Extensive experience working within major cloud ecosystems ( AWS, GCP, or Azure ). What will give you a competitive edge (Preferred Qualifications) * Advanced Experience: 8+ years of relevant industry experience. * Hardware Expertise: Working knowledge of Enterprise-grade Nvidia GPU architectures, including H100, B200, and GB200 . * Model Deployment: Experience deploying and scaling open-source models via the Hugging Face ecosystem. * Data Analytics: Proficiency in BigQuery for large-scale data analysis and reporting. * Profiling Tools: Practical experience utilizing Nvidia Nsight and Nsight Compute for kernel-level performance tuning. * Soft Skills: Strong technical communication skills with the ability to translate complex infrastructure needs into actionable business insights. Hybrid: This role is categorized as Hybrid. This means the successful candidate is expected to report to Sunnyvale Technical Center at minimum three days per week or at the hiring manager's discretion. Ability to sit remote in Seattle, WA until office opens. ## Description GM is looking for a Senior Performance Engineer to join the AV Capacity and Performance Engineering team in the AV Infrastructure org to support our critical efforts in developing autonomous vehicles. The mission of the AVCPE team is to provide input into large scale ML infrastructure strategy, advise on key decisions affecting our cloud budget, identify and execute optimization projects, and provide capacity planning and engineering expertise to support GM's efforts in developing autonomous vehicles (AV). What you'll be doing (Responsibilities) * Strategic Infrastructure Development: Adopt and run AV models to support GM's long-term GPU system strategy and "evergreen" infrastructure roadmap. * Performance Optimization: Conduct deep-dive analyses of production workloads to identify bottlenecks and propose high-impact optimization strategies. * Cross-Functional Collaboration: Partner with AI/ML Research, Infrastructure Engineering, and Cloud Vendors to spearhead projects that enhance engineering velocity and cost-efficiency. * Proactive System Scaling: Identify opportunities for architectural improvements to ensure the scalability and reliability of large-scale ML training and inference environments. ## Related Videos - [5 steps for running a Kubernetes environment at scale](https://www.wearedevelopers.com/videos/88-5-steps-for-running-a-kubernetes-environment-at-scale) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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