Senior Machine Learning Engineer

Description

The company is working out how the Earth changes by building a world model of the whole planet. The model captures how Earth processes behave, predicts what a place will look like next, and turns that understanding into something useful.

The company started at the University of Waterloo and is backed by HF0, Climactic, Onto Ventures, and angels from Firebase and OpenTable. It is a small team working on some of the biggest unsolved problems.

Responsibilities

• You will own the software and infrastructure that trains and serves the models, so research scientists can focus on the models themselves.
• Other engineers are building pipelines that ingest petabytes of earth observation and remote sensing data, plus an agent that answers customer questions using that data and the models. You own the middle: the path from ingested data into training, the systems for experiments and large-scale training, and the inference path that passes model outputs to the agent.
• You will work with those engineers to join the pieces, and with the AI research scientists to make their models trainable, measurable, and servable at scale, from small first experiments to full training runs.
• Build and run the systems for experiment tracking, distributed training at scale, and inference serving. Keep them fast, reliable, and reproducible.
• Define and maintain the boundaries between data and training, data and inference, and inference and the agent, working with engineers and scientists.
• Build the data and model plumbing needed to train one model on many sensors, observation types, and text.
• Help scientists test ideas in quick, cheap experiments, then scale the promising ones to petabyte-scale training runs.
• Build and improve internal and user-facing benchmarks and evaluation frameworks.
• Measure and reduce the cost and wall-clock time of experiments, training, and inference.
• With the product team, decide how the agent calls the model and uses its outputs, and build that integration.

Requirements

• A 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.
• Skill building distributed, multi-node training pipelines in PyTorch and Lightning.
• Experience with AWS or another cloud platform, especially the tools for training and serving medium-to-large models.
• Solid software engineering skills: you write production-quality Python and care about reliability, observability, and cost.
• Fluency with modern agentic coding tools such as Claude Code, Cursor, or Codex, and up-to-date knowledge of how to use them well.
• Bonus: experience training large diffusion or transformer models, ideally on video or time-series data.
• Bonus: experience with multimodal or spatiotemporal data, geospatial data, or geoscience problems.
• Bonus: experience serving models in production and integrating them with downstream applications or agents.
• Bonus: startup experience and comfort on a small, fast-moving team.

Benefits

• The work matters for the planet. Customers in nature conservation, carbon removal, and government use the models to protect ecosystems and respond to climate change. The company takes positive impact seriously, including safety and guardrails, and you will help get that right.
• You will join a small, technical, collaborative team founded by PhDs and engineers who have built and shipped at scale, working with people who care about the problem as much as you do.
• The company has raised over $10M+ from HF0, Inovia Capital, and Propeller Ventures, with angels including the cofounders of Firebase and OpenTable, and partners such as AWS and the United Nations.
• Competitive salary, meaningful early-stage equity, and health, dental, and vision coverage. As one of the first AI hires, your equity stake reflects that.