Senior Software Engineer, Autonomous Driving Evaluation

generalmotors

Sunnyvale (CA)

On-site

USD 150,000 - 200,000

Full time

14 days+

Application generator

Turn this role into an interview — a resume and cover letter built around what this employer wants.

Get past ATS filters

Job summary

General Motors is seeking a senior expert to advance evaluation of autonomous driving systems. You’ll develop metrics, dashboards, and scalable analyses that drive data-driven decisions across perception, prediction and planning components.

You’ll work with cross-functional teams to ensure robust, verifiable performance in real-world scenarios and simulations. You will influence architectural directions, mentor teammates, and help gate releases by delivering rigorous, explainable evaluation

Qualifications

5+ years of applied experience with robotics or autonomous systems software, data analysis, ML evaluation, or autonomy analytics.
3+ years evaluating dynamic systems using numerical and/or ML approaches, including time-series data and state derivatives.
Strong proficiency developing Python in production team environments, including testing 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 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 related field, or equivalent practical experience.

Responsibilities

Architect and implement metrics and analyses to introspect autonomous driving software performance across the autonomy stack.
Design and implement analysis algorithms that summarize, aggregate, and cluster metrics from simulations and on-road runs.
Propose and develop new statistical and ML methods to quantify performance and identify patterns across scenes and domains.
Develop and apply methods to introspect the operation of ML components in perception, prediction, and planning models.
Build and maintain autonomy evaluation dashboards and interactive reports with clear, explainable insights for development and leadership.
Leverage vision-language models and large language models to classify performance and prioritize validation efforts.
Maintain a high technical standard through system design, reviews, testing, and observability.
Interface with cross-organizational partners to articulate requirements and share best practices around evaluation and experiment design.