Autonomy Subsystem Evaluation Lead

African American Talent; Sunnyvale (CA); Hybrid; USD 219,000 - 335,000; Full time

Job summary

General Motors seeks a seasoned leader to manage Subsystem Evaluation within the Autonomy Evaluation group. You will guide metrics, data-driven decisions, and the teams delivering evaluation insights that drive vehicle autonomy improvements.

You will partner with autonomy teams, scoping questions, validating results, and presenting findings to leadership while ensuring rigorous statistical standards and trustworthy analyses.

Qualifications

• 8+ years in autonomous systems, robotics, ML evaluation, or applied data science.
• 3+ years managing engineers or data scientists, including hiring and performance management.
• Deep knowledge of autonomy metrics and their limits, such as planner quality measures, detection and tracking metrics, and trajectory prediction metrics.
• Strong statistics for evaluation, including sampling, confidence intervals, experimental design, and hypothesis testing on time-series data.
• A disciplined approach to root-causing anomalous data and unexpected system behavior.
• Production Python and SQL experience in team environments, and comfort reviewing designs and code.
• Comfort reading C++ autonomy code to understand what a subsystem actually does.
• Bachelor's, Master's, or PhD in Computer Science, Robotics, Mechanical or Aerospace Engineering, Machine Learning, Statistics, Data Science, or a related field, or equivalent practical experience.

Responsibilities

• Set the roadmap and quarterly plans for subsystem evaluation, tied to the AV program's milestones and release needs.
• Build metrics that weight subsystem errors by their effect on downstream decisions and driving, not by accuracy alone.
• Trace bad driving in simulation and road data back to the subsystem or interaction that caused it, and measure how subsystem errors add up to driving outcomes.
• Give autonomy teams and release reviews a fast, trustworthy answer to whether a software or model change helped or hurt driving, including regression detection and version-to-version comparison.
• Hold the metrics themselves to a high bar. Check them against driving outcomes and human judgment, state their confidence and limits, and retire the ones that don't hold up.
• Apply VLMs and LLMs effectively where applicable in the development/trigaging/etc processes.
• Turn results into clear findings for autonomy teams and leadership: what changed, how confident we are, and what to fix first.

Skills

Autonomous systems experience

People management

Autonomy metrics knowledge

Statistics for evaluation

Root-cause analysis

Python and SQL

C++ code reading

Influence with data

Problem solving

Advanced degree

Education

Advanced degree in CS/Robotics/ML

Tools

Python

SQL

C++

Job description

General Motors seeks a seasoned leader to manage Subsystem Evaluation within the Autonomy Evaluation group. You will guide metrics, data-driven decisions, and the teams delivering evaluation insights that drive vehicle autonomy improvements.

You will partner with autonomy teams, scoping questions, validating results, and presenting findings to leadership while ensuring rigorous statistical standards and trustworthy analyses.