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Autonomy Engineer – Performance and Reliability

EngineerEngineerFull TimeRemoteTeam 51-200Since 2016H1B SponsorCompany Site LinkedIn

Location

United States

Posted

114 days ago

Salary

Not specified

Bachelor Degree3 yrs expEnglishCloudPandasPy SparkPython

Job Description

• Lead testing strategies across routing, planning, controls, and simulation. • Collaborate closely with software developers to build robust software tests, improve testing infrastructure, and drive root cause analysis. • Design and implement testing strategies and evaluation frameworks for autonomy software systems. • Develop observability tools and test hooks within autonomy algorithms to improve testability, introspection, and debugging. • Build and maintain automated and manual test suites for simulation-based, system integration, and on-vehicle validation. • Lead test plan design, drive defect triage and root cause analysis, and surface critical risks early in the development cycle. Make recommendations for system improvements. • Increase test coverage for various autonomy behaviors and edge cases, with a focus on safety, robustness, and performance validation. • Collaborate directly with autonomy developers, infrastructure engineers, and system designers to make testing more robust. • Continuously improve test infrastructure, automation pipelines, and reporting tools to enable fast iteration, reproducibility, and actionable feedback.

Job Requirements

Bachelor’s or Master’s degree in Computer Science, Robotics, or a related technical field.
3+ years of hands-on experience in software development and testing, with a focus on complex systems such as autonomy, robotics, or real-time control.
Strong proficiency in C++, with demonstrated ability to write high-quality, testable, and maintainable code. Fluency with Python (pandas, pyspark, streamlit).
Experience designing and implementing test plans, test frameworks, and automated testing pipelines for algorithmic software (e.g., routing, planning, control systems).
Familiarity with scale testing: simulation, aggregated evaluation, hardware-in-the-loop (HIL), or cloud-based test environments.
Understanding of autonomous vehicle algorithms (e.g., path planning, motion control, kinematics, state estimation) and their specific quality and observability challenges.
Proven track record of driving root cause analysis, delivering robust fixes, and owning quality improvements across the full software lifecycle.