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Destro AI is hiring a founding robotics engineer to lead how our robots see and learn in VisionOS. You will own the perception stack, develop 3D scene understanding, and drive model deployment on real robots on customer floors.
You'll move rapidly from simulation to on-site validation, with equity stakes and a fast-growing team that values independent decision-making and end-to-end responsibility.
Location: New York / New Jersey
Office: Customer floor
Type: Full-time, founding role
Compensation: Base + equity
Travel: Significant, travel-heavy
Walk into any busy distribution center and you'll see the problem we're solving. Thousands of products, packaging that changes every season, light that shifts every time a dock door opens. Most picking and case handling is still done by hand, because robots haven't been able to make sense of a scene like that and act on it reliably.
We're hiring the engineer who will lead how our robots see and learn. You'll own perception and robot learning for VisionOS, make the core technical calls, and take our models from simulation onto real robots on customer floors.
Destro is building the intelligence layer for robotics. MothershipOS coordinates robots, people, and workflows across an entire operation in real time, and it's already running inside large enterprise operations. VisionOS brings that intelligence to the individual robot: perceiving what's in front of it, deciding what to do, and handling it. Our founders previously led teams at HAI Robotics, Dexterity, and Matic, and we closed our Seed round at one of the highest valuations for a robotics company at this stage.
This is a founding role, and you'll build this part of the product from the ground up with the founding team. There isn't a large existing codebase waiting for you. The perception team recently lost a member, and we need someone who wants to build the next version from scratch, set the technical approach, and operate with a high degree of independence.
Your work runs through the full model lifecycle: perception pipelines, VLA models, pre-training, fine-tuning, deployment, and post-training optimization all the way to inference. For us, a model is done when it works on the floor, not when the eval curve looks good.
Get to know our robots, simulation environment, and customer floors, then write the perception and learning plan you want to bet on. By the end of the month, you'll have a first model running on a physical robot doing a real task.
This role is travel-heavy. Our customer warehouses are our lab, so expect to spend a significant amount of your time on site, deploying and improving systems on live floors. If you need to be home most weeks, this probably isn't the right fit, and we'd rather say so now.