The Staff Software Engineer, Autonomy Evaluation joins the Evaluation team to build and evolve the ecosystem powering autonomous driving technology. This role involves defining strategies and architectures for metrics and analyses to evaluate software performance across the autonomy stack. You will lead cross-functional efforts to embed evaluation into development workflows, invent new statistical and ML methods to quantify performance, and own key KPIs for safety decisions. The position requires proficiency in Python and C++ within large autonomy codebases, alongside experience with numerical and ML approaches on large-scale time series data. Key tools and skills include Pandas, NumPy, SciPy, SQL, and PyTorch. You will solve the technical challenge of turning system-level results into clear feedback to accelerate validated autonomous vehicle deployment at scale.
What does a Software Engineer earn in California?
Median $214500 from 1193 postings across 76 companies.
Define the strategy and architecture for metrics and analyses to evaluate autonomous driving software performance.
Lead cross-functional efforts to embed evaluation into development workflows and release decisions.
Invent and drive new statistical and ML methods to quantify performance and detect regressions.
Own and refine key AV evaluation metrics and KPIs used for readiness and safety decisions.
Synthesize and present results and tradeoffs to stakeholders through interactive dashboards.
Manage large-scale test scenario libraries and continuous evaluation pipelines.
Develop and maintain automated workflows for data-driven decisions across AV development and verification.
What we're looking for
7+ years of applied experience with robotics or autonomous systems software across perception, planning, and control.
3+ years of experience leading evaluation of complex dynamic systems using numerical and ML approaches on large-scale time series data.
Proficiency in developing Python in production environments and the ability to work in large C++ autonomy codebases.
Proven cross-team technical leadership experience in defining strategies and influencing system and architecture decisions.
Bachelor’s, Master’s, or PhD degree in Computer Science, Robotics, Mechanical or Aerospace Engineering, Machine Learning, or a related field.
Experience in autonomous driving or high-stakes field robotics, including designing and interpreting large-scale simulation and field experiments (preferred).
Deep familiarity with statistical modeling, experimental design, and hypothesis testing, with command of Pandas, NumPy, SciPy, and visualization libraries (preferred).
Proficiency in C++, SQL, ROS, and experience with computational geometry, linear algebra, and PyTorch (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