Lead cross-functional efforts with autonomy, systems engineering, simulation, and data teams to embed evaluation into development workflows and release decisions.
Invent and drive new statistical and ML methods, and ML introspection techniques, to quantify performance, detect regressions, and reveal patterns of system behavior at scale.
Own and refine key AV evaluation metrics and KPIs used for readiness and safety decisions; synthesize and present results and tradeoffs to stakeholders; make insights readily available to partner teams through interactive dashboards.
Your Skills and Abilities (Required Qualifications)
7+ years applied experience with robotics or autonomous systems software, spanning multiple subsystems from perception through planning and control of the vehicle.
3+ years leading evaluation of complex dynamic systems using numerical and ML approaches on large-scale time series data.
Proficiency developing Python in production team environments; strong ability to work in large C++ autonomy codebases.
Proven cross-team technical leadership, including defining strategies adopted by multiple teams and influencing system and architecture decisions.
PhD, Masters, or Bachelor’s degree in Computer Science, Robotics, Mechanical or Aerospace Engineering, Machine Learning, or a related field.
What Will Give You a Competitive Edge (Preferred Qualifications)
Experience in autonomous driving or high-stakes field robotics; designing, running, and interpreting large-scale simulation and field experiments.
Deep familiarity with statistical modeling, experimental design, and hypothesis testing for autonomy evaluation; command of Pandas, NumPy, SciPy, and visualization libraries.
Proficiency in C++ and SQL, and experience shaping logging, data schemas, and evaluation pipelines for large-scale autonomy testing.
Experience working with ROS or other IPC, robotics stack logging, and with large-scale experiment databases, including designing or scaling evaluation platforms.
Prior development with computational geometry, linear algebra, PyTorch, and machine learning.
Background in modeling agent interaction and owning or designing release gating criteria and processes for autonomy systems.
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