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, ML Infrastructure engineers turn data collected from a live fleet of robots into better models. The fleet produces large volumes of video and telemetry data from real work in the field, and making that data trainable is one of the hardest systems problems at the company. As we continue to scale, these systems will require larger training runs with more data, expanded clusters, and optimal GPU utilization. WHAT YOU’LL DO - Own training and inference infrastructure. - Build the data pipelines that these training runs depend on. - Make experiments fast to launch and reproduce. - Contribute to our core training code . YOU MAY BE A GOOD FIT IF YOU: - Have built ML infrastructure that carried real production training runs. - Have scaled distributed training systems. - Strong experience with Python, PyTorch or TensorFlow. - Have experience identifying and troubleshooting GPU performance bottlenecks in large-scale training environments . 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

As we continue to scale, these systems will require larger training runs with more data, expanded clusters, and optimal GPU utilization.
Own training and inference infrastructure.
Build the data pipelines that these training runs depend on.
Make experiments fast to launch and reproduce.
Contribute to our core training code
YOU MAY BE A GOOD FIT IF YOU:
Have built ML infrastructure that carried real production training runs.
Have scaled distributed training systems.
Strong experience with Python, PyTorch or TensorFlow.
Have experience identifying and troubleshooting GPU performance bottlenecks in large-scale training environments
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