Staff ML Infrastructure Engineer, Robotics
Overview
Watney builds autonomous robots for data center construction, and you will own the ML infrastructure that turns fleet data into better models. You will drive training and inference systems handling live video and telemetry data from real work sites. Python and PyTorch or TensorFlow power your daily work. The role sits on the ML platform team, collaborating with robotics engineers and data scientists. You will scale distributed training clusters and optimize GPU utilization as the fleet grows.
What You'll Do7
- 1Design and own training and inference infrastructure that supports large-scale robotic learning
- 2Build data pipelines that feed training runs with field-collected video and telemetry data
- 3Launch experiments with fast reproducibility, cutting iteration time for model improvements
- 4Scale distributed training systems across clusters, handling growing data volumes
- 5Contribute to core training code, focusing on PyTorch or TensorFlow model execution
- 6Troubleshoot and resolve GPU performance bottlenecks in production training environments
- 7Drive infrastructure improvements to increase cluster utilization and throughput
Requirements6
- 15+ years building ML infrastructure that carried real production training runs
- 2Experience scaling distributed training systems with large data volumes
- 3Strong proficiency in Python and PyTorch or TensorFlow
- 4Proven ability to identify and fix GPU performance bottlenecks in large-scale environments
- 5Familiarity with data pipelines for video and telemetry data
- 6Experience with cluster management and scheduling tools like Kubernetes or Slurm
Salary Insight
Salary not disclosed in listing
Location
Required Skills
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