Staff Machine Learning Engineer

Watney; San Francisco (CA); On-site; USD 140,000 - 200,000; Full time

Job summary

Watney is building autonomous robotic systems to accelerate data center buildouts. The ML Engineer will turn live robot fleets into robust models, curate large-scale data, and evaluate deployments across perception, planning, navigation, and manipulation.

You will train and refine models with multimodal sensor data and help ensure low-latency inference for a live fleet. The role requires hands-on development in Python and ML frameworks, with deployment across production environments and

Qualifications

• Experience building multimodal models on GPUs with large video data.
• End-to-end dataset handling: collection, labeling, and curation.
• Production training and inference pipelines in Python (PyTorch/JAX/Triton).
• Strong debugging skills with real-world model performance evidence.
• Familiarity with cluster management systems (Ray/Slurm/Kubernetes).

Responsibilities

• Own model quality end-to-end: dataset curation, training, evaluation.
• Work with cameras, IMUs, LiDAR, and odometry data to shape training sets.
• Train and evaluate policies; run data/feature/architecture ablations.
• Deploy results to production with low-latency, 24/7 inference.
• Collaborate with Teleoperations and Fleet Operations to obtain data.

Skills

Python

PyTorch

JAX

Triton

Multimodal data

Robot perception

Tools

Ray

Slurm

Kubernetes

Git

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 engineers turn a live fleet of robots into world class modals. The fleet produces large volumes of video from real work in the field, and turning that data into a model that performs better on the next deployment is one of the hardest problems at the company.

Our Machine Learning team members run training experiments, help curate and label the data behind them, and help evaluate what works on a real fleet. You'll build models that cover the gamut of real world challenges including perception, planning, navigation and manipulation.

What You’ll Do

• 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.

You May Be a Good Fit If You:

• Trained large scale multimodal models on a fleet of GPUs using techniques such as video semantic segmentation, 6d pose estimation, VLAs and BEVs.
• Have owned 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.

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.

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