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Staff Machine Learning Engineer
Summary by AIEngineer.careers
Staff Machine Learning Engineer is a Robotics Engineer role (full-time). with Watney Robotics. in SAN FRANCISCO, US. Imported listing (source: emploive.com). Apply on the employer's site (emploive.com).
Imported listing (source: emploive.com) · Apply on emploive.com
SAN FRANCISCO · US
—
Full-time
STAFF
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from emploive.com
Watney Robotics is seeking a Staff Machine Learning Engineer to lead the development of autonomous robotic systems for data center infrastructure. The role involves end-to-end ownership of model quality, from dataset curation and training to production deployment on a live fleet of robots. You will tackle complex challenges in perception, planning, navigation, and manipulation using multimodal sensor data.
Responsibilities
- Own model quality end-to-end including dataset curation, training, and evaluation.
- Process and shape training sets using multimodal sensor data (Cameras, IMUs, LiDAR, Odometry).
- Train and evaluate policies with architecture ablations and feature engineering.
- Deploy models to production ensuring low latency, 24/7 inference capabilities.
- Collaborate with Teleoperations and Fleet Operations teams.
Requirements
- Experience training large-scale multimodal models on GPU fleets.
- Proficiency in techniques like video semantic segmentation, 6d pose estimation, VLAs, and BEVs.
- End-to-end ownership of datasets (collection, labeling, filtering).
- Production coding skills in Python using PyTorch, JAX, or Triton.
- Experience with cluster management systems such as Ray, Slurm, or Kubernetes.
Watney Robotics
San Francisco · US · 24+ employees
Watney Robotics is a robotics engineering company that builds and deploys autonomous robotic systems to operate and manage data center and supercomputing infrastructure. The company partners with major hyperscalers to help scale compute infrastructure by addressing labor shortages and operational complexity in hazardous environments.
