Staff Software Engineer, Autonomy Evaluation joins the Evaluation team to build and evolve the ecosystem powering autonomous driving technology. This role involves developing metrics, automated workflows, and analysis approaches to enable data-driven decisions across development and verification. You will define strategies for evaluating performance across the autonomy stack, lead cross-functional efforts with systems and simulation teams, and invent new statistical and machine learning methods to quantify behavior at scale. The work focuses on creating evaluation pipelines, risk assessment components, and interactive dashboards to provide feedback for release gating. Required skills include extensive experience in robotics or autonomous systems software, proficiency in Python and C++, and expertise in numerical and machine learning approaches applied to large-scale time series data. Preferred qualifications include familiarity with Pandas, NumPy, SciPy, SQL, PyTorch, and computational geometry within high-stakes field robotics contexts.
What does a Software Engineer earn in Remote?
Median $204500 from 415 postings across 57 companies.
Define the strategy and architecture for metrics to evaluate autonomous driving software performance across the entire stack.
Lead cross-functional efforts to integrate evaluation into development workflows and release decisions.
Develop new statistical and machine learning methods to quantify performance and detect system regressions.
Own and refine key AV evaluation metrics and KPIs used for safety and readiness decisions.
Synthesize and present complex results and trade-offs to internal stakeholders.
Create interactive dashboards to make evaluation insights available to partner teams.
Manage large-scale test scenario libraries and continuous evaluation pipelines.
What we're looking for
7+ years of applied experience with robotics or autonomous systems software across multiple subsystems.
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 within production team environments.
Strong ability to work within large C++ autonomy codebases.
Proven cross-team technical leadership including defining strategies and influencing system and architecture decisions.
Bachelor’s, Master’s, or PhD degree in Computer Science, Robotics, Mechanical Engineering, Aerospace Engineering, Machine Learning, or a related field.