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Staff Machine Learning Engineer

Watney Robotics· Engineering
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San FranciscoFull-timePosted 1mo ago

About the role

ML engineer role focused on turning a live fleet of robots into world-class models, handling data curation, training, evaluation, and deploying models for 24/7 low-latency inference on a fleet of autonomous robots.

Responsibilities

  • Own model quality end-to-end: dataset curation, training, and evaluation.
  • Work with multimodal sensor data from Cameras, IMUs, LiDar and Odometry to shape the training set as the fleet grows.
  • Train and evaluate policies, experiment with data, feature and architecture ablations.
  • Deploy your best results to production and build the systems that guarantee low latency, 24/7 inference on a fleet of robots.
  • Work with Teleoperations and Fleet Operations to get the data the models need.

Qualifications

  • Trained large scale multimodal models on a fleet of GPUs using techniques such as video semantic segmentation, 6d pose estimation, VLAs and BEVs.
  • Own a dataset end-to-end: what to collect, what to label, and what to cut.
  • Write production training and inference code with Python, PyTorch or JAX, Triton.
  • Have found and fixed a problem with a training run or a model's real-world performance, and can name the evidence that pointed to it.
  • Experience working on Cluster management systems including Ray, Slurm or Kubernetes.

Skills

Required:Multimodal model trainingDataset curationProduction training & inferencePythonPyTorch
Nice to have:JAXTritonRaySlurmKubernetes
Last updated Sep 19, 2026. Interested? Apply on the company site.

About Watney Robotics

Watney develops autonomous robotics systems to operate datacenter and supercomputing infrastructure.

Autonomous Physical Infrastructure

autonomous roboticsdatacentersupercomputinginfrastructurehyperscalerscompute scaling
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