Staff Software Engineer, Autonomy Evaluation
New
J
JobgetherAutonomous Vehicles
United StatesFull-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 7+ years of applied experience developing robotics or autonomous systems software; 3+ years of experience leading evaluation of complex dynamic systems.
- Required Skills
- PythonSQLMachine LearningNumpyC++Pandas
Requirements
- 7+ years of applied experience developing robotics or autonomous systems software, with experience spanning multiple subsystems from perception through planning and vehicle control.
- 3+ years of experience leading evaluation of complex dynamic systems using numerical and machine learning approaches on large-scale time-series data.
- Strong production Python development experience in collaborative engineering environments.
- Ability to work effectively within large C++ autonomy codebases.
- Proven cross-team technical leadership, including defining strategies adopted by multiple teams and influencing system and architecture decisions.
- Bachelor’s, Master’s, or PhD in Computer Science, Robotics, Mechanical Engineering, Aerospace Engineering, Machine Learning, or a related technical field.
- Deep knowledge of statistical modeling, experimental design, and hypothesis testing for autonomy evaluation.
- Strong experience with Pandas, NumPy, SciPy, and data visualization libraries.
- Proficiency in C++ and SQL, with experience shaping logging systems, data schemas, and evaluation pipelines for large-scale autonomy testing.
- Experience with ROS or other inter-process communication frameworks, robotics-stack logging, and large-scale experiment databases.
- Experience designing or scaling evaluation platforms, computational geometry, linear algebra, PyTorch, and machine learning.
Responsibilities
- Define the technical strategy and architecture for metrics, analyses, and evaluation systems that measure autonomous driving software performance across the autonomy stack.
- Lead cross-functional technical initiatives with autonomy, systems engineering, simulation, and data teams to integrate evaluation into development workflows and release decisions.
- Develop and apply new statistical, machine learning, and ML introspection methods to quantify system performance, detect regressions, and identify behavioral patterns at scale.
- Own and continuously refine key autonomous vehicle evaluation metrics and KPIs used to support readiness, safety, and release decisions.
- Analyze large-scale time-series and experimental datasets to generate meaningful system-level insights and identify areas for improvement.
- Synthesize complex evaluation results, tradeoffs, and recommendations for technical and cross-functional stakeholders.
- Build or enable interactive dashboards and analytical tools that make evaluation insights readily accessible to partner teams.
- Provide technical leadership across multiple teams, influencing system architecture, evaluation strategies, and engineering decisions.
- Help evolve large-scale evaluation platforms, scenario libraries, continuous evaluation workflows, and risk assessment processes supporting autonomous systems development.
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