Autonomy Subsystem Evaluation Manager – Remote/Hybrid

Native American Talent; Topeka (KS); Ibrido; USD 219.000 - 335.000; Tempo pieno

Descrizione del lavoro

General Motors seeks a Manager of Subsystem Evaluation to lead a team of engineers and data scientists in developing metrics and methods that quantify the impact of subsystem errors on driving. You will own the roadmap, staffing, and evaluation strategy aligned with the autonomous driving program milestones.

You will collaborate with autonomy teams, driver performance, and leadership to translate complex results into actionable guidance, ensure confidence in findings, and drive improvements

Competenze

• 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.
• Proven ability to build trusted relationships with partner teams and influence them without authority, using data they trust.
• Strong ability to turn ambiguous questions and uncertain results into clear plans and recommendations for engineering and leadership.
• 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.

Mansioni

• Set the roadmap and quarterly plans for subsystem evaluation, tied to the AV program's milestones and release needs.
• Decide which subsystems and questions come first, and what to stop.
• 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.

Conoscenze

Autonomous systems

People management

Autonomy metrics

Statistics

Root cause analysis

Python and SQL

C++ reading

Stakeholder influence

Ambiguity to plans

Education in CS/Robotics

Formazione

Bachelor’s, Master’s or PhD in CS/Robotics/Engineering/ML/Data Science

Strumenti

Python

SQL

C++

Descrizione del lavoro

General Motors seeks a Manager of Subsystem Evaluation to lead a team of engineers and data scientists in developing metrics and methods that quantify the impact of subsystem errors on driving. You will own the roadmap, staffing, and evaluation strategy aligned with the autonomous driving program milestones.

You will collaborate with autonomy teams, driver performance, and leadership to translate complex results into actionable guidance, ensure confidence in findings, and drive improvements