Senior Machine Learning Engineer to own the ML platform at Hum: experiment tracking, distributed multi-node training, and inference serving that delivers model outputs to the agent. The role defines interfaces with data and research engineers and builds evaluation frameworks. Stack includes Python, PyTorch, Lightning, and AWS.
Senior Machine Learning Engineer
Location: San Francisco or remote · Full-time
Hum’s mission is to learn how the Earth changes by building a “world model for the entire world”: one that represents the dynamics of Earth processes, forecasts what a place looks like next, and makes that understanding useful. Founded at the University of Waterloo and backed by top investors like HF0, Climactic, Onto Ventures and angels from Firebase and OpenTable, we are a small team working on some of the largest unsolved problems.
As our Senior Machine Learning Engineer, you'll own the software and infrastructure that trains and serves our models, so our research scientists can stay focused on the models themselves. The rest of our engineering team is building the pipelines that ingest petabytes of earth observation and remote sensing data, and an agent that uses that data and our models to answer customer questions. You'll own everything in between, the path from ingested data into training, the systems that run experiments and large-scale training, and the inference path that delivers model outputs to the agent. You'll work closely with those engineers to fit all the pieces together, and with our AI research scientists to make the models they dream up trainable, measurable, and servable at scale, from the first small experiment through full training runs.
