Senior Software Engineer – Autonomy Evaluation

General Motors; Sunnyvale (CA); Hybrid; USD 140,000 - 190,000; Full time

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Job summary

General Motors in Sunnyvale is seeking a highly experienced autonomy evaluation engineer to design metrics, analyses, and dashboards across the autonomy stack, collaborating with cross-functional teams.

You will develop ML evaluation methods, validate perception/prediction/planning models, and help drive data-driven decisions for scalable AV deployment.

Responsibilities include building dashboards, applying VLM/LLM where appropriate, and upholding software engineering standards.

Qualifications

• 5+ years of robotics or autonomous systems software experience.
• 3+ years evaluating dynamic systems with time-series data.
• Strong Python production experience, testing and code reviews.
• Proficiency with Pandas, NumPy, SciPy and visualization libraries.
• C++ reading, debugging, and instrumenting core algorithms.
• Proven leadership and cross-team influence.
• Advanced degree or equivalent practical experience in related fields.

Responsibilities

• Architect and implement metrics and analyses for autonomy performance across the stack.
• Design analysis algorithms for simulations and on-road data.
• Develop new statistical and ML methods to quantify performance.
• Evaluate ML components including perception, prediction, and planning.
• Build and maintain autonomy evaluation dashboards and reports.
• Collaborate with cross-organizational partners to share requirements and best practices.
• Adhere to software engineering standards, testing, and observability.

Skills

Robotics experience

Python production experience

C++ proficiency

Data analysis

Leadership

Communication

Education

Bachelor's/Master's/PhD in CS/Robotics or related field

Tools

Python

Pandas

NumPy

SciPy

ROS

SQL

PyTorch

Job description

About the Organization

The Evaluation team builds and evolves the evaluation ecosystem that powers the development and scaling of 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 for engineering and leadership, and help accelerate validated AV deployment at scale.

What You’ll Do (Responsibilities)

• Architect and implement metrics and analyses to introspect autonomous driving software performance at subsystem interfaces across the autonomy stack; partner closely with autonomy developers and systems engineers.
• Design and implement analysis algorithms that summarize, aggregate, and cluster metrics produced by simulations and on-road runs of the autonomy stack.
• Propose and develop new statistical and ML methods to quantify performance and identify patterns of system and subsystem behavior across diverse scenes and operational domains.
• Develop and apply methods to introspect the operation of ML components in the autonomy stack, including evaluation of perception, prediction, and planning models.
• Build and maintain autonomy evaluation dashboards and interactive reports that provide clear, explainable insights (e.g., trend analysis, drift detection, scenario coverage) for development, verification, and leadership.
• Leverage vision-language models (VLMs) and large language models (LLMs), where appropriate, to classify autonomy performance, identify critical scenarios, and prioritize validation efforts, integrating human-in-the-loop review where needed.
• Maintain a high technical standard through thoughtful system design, code reviews, testing, observability, and adherence to software-engineering best practices.
• Interface with cross-organizational partners to articulate requirements, resolve handoff issues, and share best practices around evaluation, metrics, and experiment design.

Your Skills & Abilities (Required Qualifications)

• 5+ years of applied experience with robotics or autonomous systems software (e.g., sensors, perception, prediction, planning, or control), data analysis, ML evaluation, or autonomy analytics.
• 3+ years evaluating dynamic systems using numerical and/or ML approaches, including time-series data, state derivatives, dynamics, and interconnected subsystems.
• Strong proficiency developing Python in production team environments, including testing, performance, and code review.
• Proficiency with Pandas, NumPy, SciPy, and plotting/visualization libraries for large-scale data analysis and reporting.
• Comfort working with C++ codebases, including reading, debugging, and instrumenting core algorithms.
• A strong curiosity to question anomalous data and systematically root-cause discrepancies.
• Demonstrated technical leadership, including driving architectural decisions, influencing cross-team designs, and owning complex features or services end-to-end.
• Bachelor's, Master's, or PhD in Computer Science, Robotics, Mechanical or Aerospace Engineering, Machine Learning, Data Science, or a related field, or equivalent practical experience.

What Will Give You a Competitive Edge (Preferred Qualifications)

• Experience in autonomous driving or field robotics, including visualizing and interpreting results from simulation and field experiments.
• Experience evaluating robotics or AV systems using sensor data (e.g., camera, lidar, radar) and large-scale time-series analysis.
• Strong intuition for data visualization and the ability to decompose high-dimensional metrics into clear, trustworthy, and consumable views for technical and non-technical audiences.
• Familiarity with statistical modeling, experimental design, and hypothesis testing for autonomy or simulation evaluation; fluency with Pandas, NumPy, SciPy, and visualization tools.
• Proficiency in C++ and SQL; experience shaping logging, data schemas, and evaluation pipelines for large-scale autonomy testing and performance monitoring.
• Experience working with ROS or similar robotics/IPC frameworks, log pipelines, and large-scale experiment databases or evaluation platforms.
• Prior development experience with computational geometry, linear algebra, PyTorch, and ML techniques applied to perception, prediction, planning, or control.
• Background in modeling agent interaction and contributing to release gating and safety decisions for autonomy systems.
• Experience leveraging AI-assisted development and analytics tools to improve productivity and evaluation coverage.

About GM

Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.

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