Staff Software Engineer - Autonomy Evaluation

General Motors LLC; Sunnyvale (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

General Motors is building and evaluating autonomous driving software with a dedicated Evaluation team in Sunnyvale. You will help shape scalable evaluation platforms, define metrics, and turn system results into actionable feedback for AV development and release decisions.

The role focuses on leading cross-functional efforts and inventing ML-based evaluation approaches to quantify performance and detect regressions at scale.

Qualifications

• 7+ years of applied robotics/autonomy software experience across perception to planning.
• 3+ years leading evaluation of complex dynamic systems with ML on time series data.
• Production Python development; strong C++ experience in large autonomy codebases.
• Cross-team leadership and ability to influence architecture decisions.

Responsibilities

• Define metrics and analyses strategy to evaluate autonomy software performance.
• Lead cross-functional teams to embed evaluation into development workflows.
• Invent ML methods and introspection to quantify performance and detect regressions.
• Own AV evaluation metrics and present results to stakeholders with dashboards.

Skills

Robotics software

Python production

C++ autonomy codebase

Cross-team leadership

Data analysis

Problem solving

Education

PhD / Masters / Bachelor in CS/Robotics/Engineering

Tools

Pandas

NumPy

SciPy

PyTorch

ROS

SQL

Job description

Job Description

General Motors is a global leader in advanced driver assistance. With Super Cruise hands-free technology in more than 500,000 Super Cruise-equipped vehicles on the road, and over 700 million hands-free miles driven, GM is proving that automation can be trusted, intuitive, and helpful. GM has the global reach to bring cutting-edge advances to everyday drivers at unprecedented scale. Join us to help deliver the next generation of safe and delightful personal autonomous vehicle experiences. The Evaluation team builds and evolves the evaluation ecosystem that powers developing and scaling GM’s autonomous driving technology. We develop metrics, automated workflows, and analysis approaches that enable data-driven decisions across AV development and verification. Partnering with Autonomy, Simulation, Systems, and Safety teams, we act as system-level integrators and arbiters of end-to-end AV quality. We own large scale test scenario libraries, continuous evaluation pipelines, and critical risk assessment and release gating components, treating road testing, data mining, training, and metrics as first-class use cases in a unified analytics framework. By joining this team, you will help shape GM’s core evaluation platforms, turn system-level results into clear feedback, and help accelerate validated AV deployment at scale.

What You’ll Do (Responsibilities)

• Define the strategy and architecture for metrics and analyses to evaluate autonomous driving software performance across the autonomy stack.
• Lead cross-functional efforts with autonomy, systems engineering, simulation, and data teams to embed evaluation into development workflows and release decisions.
• Invent and drive new statistical and ML methods, and ML introspection techniques, to quantify performance, detect regressions, and reveal patterns of system behavior at scale.
• Own and refine key AV evaluation metrics and KPIs used for readiness and safety decisions; synthesize and present results and tradeoffs to stakeholders; make insights readily available to partner teams through interactive dashboards.

Your Skills & Abilities (Required Qualifications)

• 7+ years applied experience with robotics or autonomous systems software, spanning multiple subsystems from perception through planning and control of the vehicle.
• 3+ years leading evaluation of complex dynamic systems using numerical and ML approaches on large-scale time series data.
• Proficiency developing Python in production team environments; strong ability to work in large C++ autonomy codebases.
• Proven cross-team technical leadership, including defining strategies adopted by multiple teams and influencing system and architecture decisions.
• A strong curiosity to question anomalous data and systematically root-cause discrepancies.
• PhD, Masters, or Bachelor’s degree in Computer Science, Robotics, Mechanical or Aerospace Engineering, Machine Learning, or a related field.

What Will Give You a Competitive Edge (Preferred Qualifications)

• Experience in autonomous driving or high-stance field robotics; designing, running, and interpreting large-scale simulation and field experiments.
• Deep familiarity with statistical modeling, experimental design, and hypothesis testing for autonomy evaluation; command of Pandas, NumPy, SciPy, and visualization libraries.
• Proficiency in C++ and SQL, and experience shaping logging, data schemas, and evaluation pipelines for large-scale autonomy testing.
• Experience working with ROS or other IPC, robotics stack logging, and with large-scale experiment databases, including designing or scaling evaluation platforms.
• Prior development with computational geometry, linear algebra, PyTorch, and machine learning.
• Background in modeling agent interaction and owning or designing release gating criteria and processes for autonomy systems.

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