Autonomous Driving Systems Engineer

Auto Mid-level (L4 equivalent) 2-5 years

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Tesla

Rivian

Lucid Motors

Ford

Waymo

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The job description

Tech stack. ROS 2, C++, Python, sensor fusion (Kalman filters), CARLA/LGSVL simulation, LiDAR/camera/radar stacks, AUTOSAR Adaptive

About the role

You will join the autonomy team of a company building self-driving capability for passenger vehicles or robotaxis. The team owns the full stack from perception through planning to vehicle control, and your work sits at the system level where sensors, compute, and actuation meet. This role matters because autonomy is a systems problem: the best perception model in the world means nothing if the vehicle cannot execute a safe, comfortable maneuver, and your integration makes the stack drivable. You will work shoulder to shoulder with safety engineers to argue the safety case for every release, backing claims with fleet data rather than intuition. Your scenario library will grow from real disengagements and near misses, turning each field event into regression coverage the whole team benefits from. You will also partner with systems engineers on compute and power budgets, because every teraflop of perception has a thermal and cost consequence in a production vehicle.

What you will achieve

Deliver an autonomy subsystem (perception, prediction, planning, or controls) from prototype to road-tested release with disengagement rates cut in half
Build simulation scenarios covering 10,000+ edge cases, catching regressions before they reach the test fleet
Own sensor fusion or planning module performance: latency budgets under 100 ms, tracked against real-world drive data
Cut on-road testing cost per validated mile by 30 percent through targeted scenario extraction from fleet logs and synthetic data
Drive safety case evidence with hazard analysis, requirements traceability, and validation reports aligned to UL 4600 or ISO 26262

What you will bring

Must-haves

2 to 5 years of experience in autonomous driving, robotics, or ADAS development
Strong C++ and Python skills for production-quality autonomy software
Deep understanding of at least one autonomy domain: perception, sensor fusion, motion planning, or vehicle controls
Familiarity with ROS 2, sensor drivers, and time synchronization across LiDAR, camera, and radar
Experience with simulation tools (CARLA, LGSVL, or proprietary) for scenario-based testing
Working knowledge of Kalman filtering, state estimation, or trajectory optimization as applicable to your domain
Rigorous testing mindset: you validate in sim, on closed course, and on public road in that order

Nice-to-haves

Experience with automotive middleware (AUTOSAR Adaptive, DDS, or SOME/IP)
Knowledge of functional safety standards (ISO 26262, SOTIF ISO 21448)
Familiarity with HD maps and localization (GNSS/INS, NDT, or particle filters)
Contributions to open-source robotics or autonomy projects