Senior AI/ML Performance Engineer — Scale GPU Infra

Aimlroles

Sunnyvale (CA)

Hybrid

USD 145,000 - 261,000

Full time

14 days+

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Benefits offered by this job

Hybrid work model

Health & wellbeing benefits

Job summary

General Motors is seeking a Senior Performance Engineer to join the AV Capacity and Performance Engineering team. You will help develop and optimize large-scale ML infrastructure and GPU platforms for autonomous-vehicle research and deployment.

Responsibilities include architectural improvements, performance analysis, and cross-functional collaboration with AI/ML research, infrastructure engineering, and cloud vendors to improve velocity and reduce costs.

Qualifications

5+ years of professional experience in high-scale infrastructure or ML systems.
Bachelor's Degree in Computer Science, a related technical field, or equivalent practical experience.
Expert-level coding skills in Python and the ability to architect/debug within the PyTorch ecosystem.
Proven track record of resolving performance issues within large-scale distributed production environments.
Deep understanding of distributed systems, modern ML system design and HPC.
Hands-on experience with Kubernetes for orchestrating complex workloads.
Technical proficiency with Nvidia DCGM, nvidia-smi, and Grafana for telemetry and observability.
Extensive experience in cloud ecosystems (AWS, GCP, or Azure).
Strong technical communication skills to translate infrastructure needs into business insights.

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.