Job description

OUR MISSION Expand human ambition in the physical world. Critical infrastructure is constrained by labor shortages, hazardous working conditions, and operational complexity. Watney builds and deploys autonomous robotic systems that increase the speed and capacity of buildout, starting with data centers. ABOUT THE ROLE At Watney, research engineers decide what our models learn to do next, and a live fleet of robots is the only test that counts. The plan is a staged path from imitation learning toward broader generalization, with each stage expected to yield a working model before the next one starts. You will help set that path: which method to try, and whether a result actually holds up outside a benchmark. WHAT YOU’LL DO - Decide which training method to try next as the model roadmap advances. - Design the evaluation that tells you whether a result generalizes or just fits the case it was built for. - Turn a promising result into something that runs on the fleet, working with the team that owns training and serving infrastructure. YOU MAY BE A GOOD FIT IF YOU: - Have taken a training idea from a paper or a prototype to a result that held up outside a benchmark. - Have worked with imitation learning, reinforcement learning, or a similar method for control, and can describe how you knew it was working or wasn't. - Are comfortable when no method is settled yet and you are one of the people deciding it. - Write production ML code (Python, PyTorch, etc.). We’re committed to building a diverse, inclusive team. At Watney Robotics, we welcome people of all backgrounds and identities, and we make hiring decisions based on skills, experience, and potential. If you’re passionate about robotics but don’t meet every requirement, we still encourage you to apply! CURIOUS TO LEARN MORE? Follow us here on X x.com and LinkedIn linkedin.com

Requirements

Must-haves

You will help set that path: which method to try, and whether a result actually holds up outside a benchmark.
Decide which training method to try next as the model roadmap advances.
Design the evaluation that tells you whether a result generalizes or just fits the case it was built for.
Turn a promising result into something that runs on the fleet, working with the team that owns training and serving infrastructure.
YOU MAY BE A GOOD FIT IF YOU:
Have taken a training idea from a paper or a prototype to a result that held up outside a benchmark.
Have worked with imitation learning, reinforcement learning, or a similar method for control, and can describe how you knew it was working or wasn't.
Are comfortable when no method is settled yet and you are one of the people deciding it.
Write production ML code (Python, PyTorch, etc.).
CURIOUS TO LEARN MORE?

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlpython

Platform: ashby
Effort: quick