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.

• Own the ML platform. Build and run the systems for experiment tracking, distributed training at scale, and serving inference, and keep them fast, reliable, and reproducible.

• Collaborate on the interfaces. Work with our engineers and scientists to define and maintain the boundaries between data and training, data and inference, and inference and the agent.

• Enable multimodal training. Build the data and model plumbing that lets us train on many sensors, observation types, and text in one model.

• Go from small to large. Help our scientists prove ideas in fast, cheap experiments, then scale the winners to petabyte-scale training runs.

• Make evaluation real. Implement and improve our benchmarks and evaluation frameworks, both internal and user-facing.

• Watch the meter. Measure and optimize the cost and wall-clock time of experiments, training, and inference.

• Support the agent. Work with the product team on how the agent calls the model and consumes its outputs, and build the integration.

• Bachelor's degree in computer science, engineering, or a related field, or equivalent experience.

• 5+ years of relevant industry experience, including hands-on machine learning engineering.

• Proficiency building distributed, multi-node training pipelines in PyTorch and Lightning.

• Experience with AWS or another cloud platform, particularly the tooling for training and serving medium-to-large models.

• Strong software engineering fundamentals: you write production-quality Python and care about reliability, observability, and cost.

• Fluent with modern agentic coding tools (e.g. Claude Code, Cursor, Codex) and current on how to use them well.

• Experience training large diffusion or transformer models, ideally on video or time-series data.

• Experience with multimodal or spatiotemporal data, geospatial data, or geoscience problems.

• Experience serving models in production and integrating them with downstream applications or agents.

• Startup experience, comfortable with a small, fast-moving team.

• Work that matters for the planet. Our customers in nature conservation, carbon removal, and government use our models to protect ecosystems and respond to a changing climate. We take positive impact seriously, including safety and guardrails, and you'll help us get it right.

• A team worth joining. Founded at the University of Waterloo by PhDs and engineers who've built and shipped at scale, we're small, technical, and collaborative. You'll work alongside people who care about the problem as much as you do.

• Well backed. $10M+ raised from HF0, Inovia Capital, and Propeller Ventures, angels including the cofounders of Firebase and OpenTable, and partners like AWS and the United Nations.

• Compensation and upside. Competitive salary, meaningful early-stage equity, health, dental and vision. As one of the first AI hires, your equity stake reflects that.