Senior Machine Learning Engineer

📋 Job Overview

Senior Machine Learning Engineer

Location: San Francisco

About Hum.ai
Hum.ai is building planetary superintelligence. Backed by top funds, we’ve raised $10M+ and are now heads down building.

Join us at the cutting edge, where we’re scaling generative transformer diffusion models, designing next-gen benchmarks, and engineering foundation models that go far beyond LLMs. You’ll be at the core of a moonshot journey to define what’s next in agentic AI and frontier model capabilities.

🏢 About Hum.ai

Hum is a seed-funded startup on a mission to create positive impact through earth observation and AI. Founded at the University of Waterloo by a team of PhDs and engineers, we’re backed by some of the best AI and climate tech investors like HF0, Inovia Capital and Propeller Ventures, angels like James Tamplin (cofounder Firebase) and Sid Gorham (cofounder OpenTable, Granular), and partners like Amazon AWS and the United Nations.

🎯 The Role

We’re building multimodal foundation models for the natural world. We believe there’s more to the world than the internet + more to intelligence than memorizing the internet. Our models are trained on satellite remote sensing and real world ground truth data, and are used by our customers in nature conservation, carbon dioxide removal, and government to protect and positively impact our increasingly changing world. Our ultimate goal is to build AGI of the natural world.

📌 Required Qualifications

• Bachelor’s degree in computer science, engineering, a related field, or equivalent experience.
• 5+ years of relevant work experience.
• Prior experience building distributed training pipelines for multi-node systems using PyTorch and Ray.
• Experience training large diffusion or transformer models. Preferably on video or time series data.
• Proficiency with Python, Ray Trainer, PyTorch, and Anyscale framework.
• Familiarity with cloud platforms such as AWS, GCP, or Azure.

⭐ Desirable Experience

• Past training of video or time-series models
• Startup experience, comfortable with a small dynamic team.
• Location wise, strong preference for in-person in Waterloo or San Francisco however remote work is possible for exceptional candidates.