Tesla·Machine Learning Engineer·Technical Phone Screen
Apr 2026
Tesla ML Engineer technical screen, pretty deep into autonomous driving reward design. Two questions but they were both layered enough that the conversation stretched way longer than I expected, especially the follow-up on non-stationary speed limits which went in a direction I didn't anticipate at all.
- Given raw trajectory data of shape [batch, num_waypoint, 2] sampled at 10 Hz, implement an RL reward function that enforces speed limits. Give two variants: one that counts or sums waypoints exceeding the limit, and one that penalizes by the magnitude of the speed violation.
- How would you handle a speed limit that changes mid-trajectory, say 50 mph for the first 2 seconds and then 30 mph for the next 3 seconds? And specifically, the interviewer pushed back on just recomputing reward with state-dependent limits. What should the policy actually do, and how do you design the reward and observations to support that?
“The coding part was fine.”