The Senior Software Engineer – Autonomy Evaluation joins the Evaluation team to build and evolve the ecosystem powering autonomous driving technology. This role involves architecting metrics and analysis algorithms to introspect software performance across the autonomy stack, specifically focusing on perception, prediction, and planning models. You will design automated workflows, develop statistical and ML methods to quantify system behavior, and build interactive dashboards for trend analysis and drift detection. Key responsibilities include managing large-scale test scenario libraries and continuous evaluation pipelines. Required skills include proficiency in Python, Pandas, NumPy, and SciPy, along with the ability to read and debug C++ codebases. The work addresses the technical challenge of turning complex system-level results into clear feedback for engineering and leadership to accelerate validated autonomous vehicle deployment.
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Architect and implement metrics to analyze autonomous driving software performance across the autonomy stack.
Design and implement algorithms to summarize and cluster metrics from simulations and on-road runs.
Develop statistical and ML methods to quantify performance and identify patterns across diverse operational domains.
Build and maintain evaluation dashboards and interactive reports to provide clear insights for leadership.
Leverage vision-language models and large language models to classify performance and prioritize validation efforts.
Maintain high technical standards through system design, code reviews, testing, and software-engineering best practices.
Interface with cross-organizational partners to articulate requirements and share best practices for experiment design.
What we're looking for
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 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.
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 a related field, or equivalent practical experience.
Experience in autonomous driving or field robotics, including visualizing and interpreting results from simulation and field experiments (preferred).
Python
C++
SQL
PyTorch
Pandas
NumPy
SciPy
ROS
Machine Learning
Data Analysis
Time-series Analysis
Statistical Modeling
Computational Geometry
Linear Algebra
Observability
Data Mining
Data Visualization