Senior AI/ML Performance Engineer

General Motors (GM)

Confirmed live yesterday High trust
Hybrid

Quick summary

Work type
Hybrid
Location
Sunnyvale, CASeattle, WA
Salary
$144,700–$261,300 / yr
Posted
25 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $211k
This role $203k
$131k most similar roles pay here $277k

This role pays less than 62% of similar roles. Most pay $175,593–$246,150 — the shaded band above. At the midpoint, this role pays about $203k versus about $211k for comparable roles.

Based on 240 similar postings.

Employer

About General Motors (GM)

General Motors (GM) is a leading American multinational automotive corporation founded in 1908 and headquartered in Detroit, Michigan.

General Motors (GM) currently has 116 open roles on FindRole.

Listed pay typically runs $160,200–$245,000 across 59 roles with salary data.

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View all roles at General Motors (GM)

At a glance

TL;DR · Senior AI/ML Performance Engineer

As a Senior AI/ML Performance Engineer within the AV Capacity and Performance Engineering team, you will support critical efforts in developing autonomous vehicles by managing large-scale ML infrastructure strategy. You will perform deep-dive analyses of production workloads to identify bottlenecks, execute optimization projects, and provide capacity planning for training and inference environments. Your daily work involves collaborating with research teams and cloud vendors to improve engineering velocity and cost-efficiency while ensuring system scalability. The role requires expert-level Python coding within the PyTorch ecosystem, along with experience in Kubernetes, distributed systems, and high-performance computing. You will utilize tools such as Nvidia DCGM, Grafana, BigQuery, and Nvidia Nsight for telemetry and kernel-level tuning. Technical requirements include proficiency with major cloud platforms like AWS, GCP, or Azure, and knowledge of enterprise-grade Nvidia GPU architectures.

What you'll do

  • Execute and manage AV models to support long-term GPU system strategy and infrastructure roadmaps.
  • Conduct deep-dive analyses of production workloads to identify bottlenecks and propose optimization strategies.
  • Provide capacity planning and engineering expertise to support autonomous vehicle development.
  • Identify architectural improvements to ensure the scalability and reliability of large-scale ML training environments.
  • Manage cloud budget decisions and advise on high-level infrastructure strategy.
  • Partner with internal teams and cloud vendors to improve engineering velocity and cost-efficiency.
  • Monitor real-time telemetry and observability for GPU systems using tools like Grafana and Nvidia DCGM.

What we're looking for

  • Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience.
  • 5+ years of professional experience in high-scale infrastructure or ML systems.
  • 8+ years of relevant industry experience.
  • Expert-level coding skills in Python and the ability to architect/debug within the PyTorch ecosystem.
  • Proven track record of resolving performance issues in large-scale distributed production environments.
  • Deep understanding of distributed systems, modern ML system design, and high-performance computing (HPC).
  • Hands-on experience with Kubernetes for orchestrating complex workloads.
  • Technical proficiency with Nvidia DCGM, nvidia-smi, Grafana, and major cloud platforms like AWS, GCP, or Azure.

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