Backed by Y Combinator.
About the role
You will own one research direction on the production planner, working with the researchers who ship it, on real fleet data, and present your results to the people who decide what ships. What a successful planner means to us, and what we will measure you against: Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Understands what is missing: finds the mechanism behind a driving planner's failures and shows which gap matters before changing anything
- Handles the long tail, never gets stuck and knows how to get itself unstuck: agile, it commits, launches, re-plans and recovers on its own
- Collision- and comfort-aware, and generalizes across structured environments (roads, lanes, intersections) and unstructured ones (lots, yards, sites, robotics settings)
- Knows how to test and evaluate actuation and planning pipelines: open-loop, closed-loop and on-vehicle, with metrics that predict road behavior
- Production quality, not a proof of concept: tested code, reproducible results, behavior that holds across checkpoints and scenes. This is our standard and our measure of success
More about this role
Zendar builds a radar-centric autonomy stack which makes any vehicle - from cars to robots - autonomous in any environment. With our deep radar DNA, we have architected our solution to put RF sensing at the core of all perception. The result is a system that handles long range, high speeds, and bad weather not as edge cases but as a core strength of the autonomy stack.
Because radars naturally measure both 3D position and velocity for every object in the environment, radar-centric autonomy is extremely compute- and data-efficient. Our autonomous vehicle needs only a few thousand dollars of hardware to make it completely autonomous, making this the cheapest way to build an autonomous vehicle by far.
See a demo of Zendar’s foundational RF perception and driving functions .
To develop this capability we had to build the entire stack in house - from radar sensor hardware to signal processing to multi-modal perception foundation models and path and trajectory planning. As part of a small team, you will have a front-row seat to seeing how a complete autonomy stack is architected and how your engineering decisions improve the ability to navigate autonomously in the real world.
Although...
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