Applied ML Validation Manager

General Motors (GM)

Remote

Quick summary

Work type
Remote
Location
Remote
Salary
$218,000–$335,000 / yr
Posted
10 days ago

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $216k
This role $276k
$141k most similar roles pay here $356k

This role pays more than 90% of similar roles. Most pay $181,905–$249,750 — the shaded band above. At the midpoint, this role pays about $276k versus about $216k for comparable roles.

Based on 239 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 126 open roles on FindRole.

Listed pay typically runs $170,000–$258,500 across 75 roles with salary data.

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At a glance

TL;DR · Applied ML Validation Manager

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As an Applied ML Validation Manager at the Autonomous Vehicle organization’s Software Validation team, you will lead a team responsible for developing and operating behavior critics and human benchmarking capabilities to ensure that machine learning-driven autonomy systems meet rigorous safety and performance standards. Your daily tasks include defining evaluation frameworks, integrating behavior critic signals into validation pipelines, and collaborating with various teams to enhance model development and release decisions. The role requires expertise in Python, PyTorch, and experience with simulation-based validation for ML systems. Ideal candidates have a background in computer science or related fields, 8+ years of relevant experience, and 2+ years of people management in engineering or applied ML roles. This position involves working on cutting-edge technologies to solve complex challenges in autonomous vehicle testing at scale.

Skills

Python PyTorch Evaluation/validation pipelines Metrics definition Simulation-based validation VLM critics Human benchmarking Agentic workflows ML observability

What you'll do

  • Lead the development and operation of behavior critics for ML-driven autonomy systems.
  • Define evaluation strategies to assess ML system performance against human-like driving expectations.
  • Design and implement frameworks that convert qualitative human feedback into quantitative metrics.
  • Develop scalable programs to benchmark ML system performance relative to expert human drivers.
  • Integrate behavior critic outputs into validation pipelines and continuous release processes.

What we're looking for

  • 8+ years experience with MS/PhD in Computer Science, Machine Learning, Robotics, Software Engineering, or Data Science.
  • 2+ years people management experience leading engineering, validation, or applied ML teams.
  • Strong programming skills in Python and proficiency with PyTorch and other ML tooling.
  • Experience designing and operating evaluation/validation pipelines for complex ML systems.
  • Proven ability to define, implement, and track metrics for system quality, reliability, safety, or user experience.

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