Backed by Y Combinator.
About the role
We are hiring a Software Engineer to join the team and you will own components from algorithm design through real-time C++ implementation, simulation, tuning, and on-vehicle validation. Because the team is small and the surface area is large, this role is ideal for someone with strong software fundamentals, a solid grounding in robotics or controls, and genuine eagerness to see their code move a physical vehicle.
What they're looking for
- Reason about interactions with other road users and translate desired driving behavior into algorithmic changes across the planning stack
- Develop Vehicle Control Software Architect, implement, and validate control and estimation algorithms for the vehicle's longitudinal and lateral dynamics
- Write mission-critical, real-time C++ that runs on-vehicle and on embedded automotive compute
- Integrate with Perception and Platform Work closely with our Perception and Software Platform teams to define clean interfaces between perception output, planning, and control
- Support driving-function demos on our vehicles, from bring-up through customer-facing runs
- Test, Measure, and Improve Build simulation and analysis tooling in Python and C++ to evaluate planner and controller performance before code ever touches a vehicle
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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