Location: SF or Waterloo, with ability to travel
Start Date: Flexible, ideally Q3 2025
Hum.ai is building planetary superintelligence. Backed by top funds, weve raised $10M+ and are now heads down building.
Join us at the cutting edge, where were scaling generative transformer diffusion models, designing next-gen benchmarks, and engineering foundation models that go far beyond LLMs. Youll be at the core of a moonshot journey to define whats next in agentic AI and frontier model capabilities.
We are looking for an experienced Senior Machine Learning Engineer who is eager to advance the frontier of AI, help us design, build, and scale end-to-end novel foundation models, and leverage their hands-on experience implementing a wide range of pre-training and post-training models, including large foundation models (beyond just LLM fine-tuning).
This role is focused on:
Designing, implementing, and scaling state-of-the-art models
Productionizing research codes, models and technologically complex systems
Shaping benchmark design and model evaluation frameworks
Building agentic AI capabilities and long-term technical bets
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, were 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.
Were building multimodal foundation models for the natural world. We believe theres 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.
The role will involve:
Collaborating with researchers and scientists to implement, evaluate and scale proof-of-concept models.
Owning, implementing and integrating the latest state-of-the-art methods and external open-source codes.
Develop AI systems capable of accurately understanding the universe and generating new knowledge.
Training multi-modal models supporting different sensor and other modalities like text
Bachelors 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.
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.
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