---
title: 'Senior Machine Learning Engineer at hum.ai'
canonical: 'https://feeny.ai/job/senior-machine-learning-engineer-hum-ai-san-francisco-q602awfh5q5v'
type: 'job'
last_seen: '2026-09-09'
---

# Senior Machine Learning Engineer at hum.ai

- **Company:** hum.ai
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Posted:** 2025-05-20
- **Last confirmed live:** 2026-09-09
- **Apply:** https://jobs.ashbyhq.com/hum-ai/7bf66ca3-d1b3-4819-8279-6d1f1616a27a

## Job description

## SENIOR MACHINE LEARNING ENGINEER

Location: San Francisco

ABOUT HUM.AI http://Hum.ai

Hum.ai http://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.

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

WHO ARE WE?

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.

WHAT DO WE DO?

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.

## ABOUT THE ROLE

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

## REQUIREMENTS

- 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.

## NICE TO HAVE

- 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.

## About hum.ai

## Company Overview
- **One-liner**: hum.ai builds multimodal foundation models trained on satellite remote sensing and real-world data, aiming to develop artificial general intelligence (AGI) for the natural world.
- **Entity Type**: Private (Pre-Seed)
- **Headquarters**: San Francisco, California, United States (with a presence in Kitchener, Ontario, Canada)
- **Founded**: 2022
- **Founders**: Thomas Storwick (COO) and Kelly Zheng (CEO)

## Core Business
- **Primary industry/industries**: Artificial Intelligence, Climate Tech, Earth Observation, Deep Tech
- **Target customers**: B2B; nature conservation organizations, carbon dioxide removal (CDR) project developers, and government agencies
- **Mission or purpose statement**: To develop AGI of the natural world, building intelligence that goes beyond memorizing the internet by tapping into satellite remote sensing and real-world data to protect and positively impact our changing world.

## Products & Services
- **Multimodal Foundation Models (Earth Observation)**: The company’s core product is a set of large AI models trained on satellite imagery and ground-truth data. These models are designed to analyze the physical world, enabling applications like quantifying seaweed biomass for blue carbon credits, monitoring deforestation, and assessing ecosystem health. The delivery mechanism is likely an API or custom deployment, with Amazon Web Services (AWS) and the United Nations listed as partners.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed
- **Key Metric**: The company was formerly known as Coastal Carbon, under which it secured a reported **$1.6 million in funding**. Current total funding for hum.ai is undisclosed.
- **Notable Investors/Partners**: F4 Fund, HF0, Inovia Capital, Propeller Ventures; partners include Amazon Web Services (AWS) and the United Nations.
- **Growth Signals**: The company rebranded from Coastal Carbon to hum.ai in 2024-2025, signaling a strategic pivot from a specific carbon-credit verification tool to a broader foundation model platform. The team includes PhDs and engineers from top institutions (Vector Institute, Mila, University of Waterloo). Active recruitment for AI Research Scientists and Machine Learning Engineers points to continued investment in core R&D.

## Competitive Advantages
- **First-mover in "AGI for the natural world"**: While many companies apply AI to satellite data, hum.ai is explicitly pursuing a general-purpose foundation model approach, differentiating from narrower application-specific tools.
- **Technical moat**: The team’s deep expertise in both frontier AI (multimodal models) and physical sciences (chemical engineering, nanotechnology, earth sciences) creates a unique capability to bridge the gap between massive compute and real-world sensor data.
- **Backing from top-tier specialized investors**: Syndicate of F4 Fund, HF0, Inovia, and Propeller Ventures provides credibility and access to the AI and climate tech ecosystems.
- **Institutional partnerships**: Collaboration with AWS and the United Nations lends validation and potential for large-scale deployment.

## Strategic Focus
- **Current priorities**: Transitioning from research prototype to named commercial contracts and a clearer product roadmap. The company is actively hiring for research and engineering roles to scale its foundation model development.
- **Direction for growth**: Deepening its multimodal model capabilities, expanding into additional government and conservation use cases, and moving toward its stated goal of AGI for the natural world.

## Why Work Here
- **Culture highlights**: The team is described as a group of PhDs and engineers passionate about applying cutting-edge AI to solve real-world environmental problems. The work is research-intensive and mission-driven, with a focus on positive climate impact.
- **Remote/hybrid/office policy**: The company operates as a remote-first organization, with team members distributed across the United States and Canada. There is a physical presence in San Francisco, CA, and Kitchener, Ontario.
- **Notable perks or engineering culture**: Opportunity to work on frontier AI models (multimodal, foundation models) with access to high-performance computing resources (NVIDIA). The small, early-stage team (7 employees) offers significant ownership and impact. The work sits at the intersection of AI and climate tech, two of the highest-conviction technology themes.

## Sources
1. [hum.ai LinkedIn](https://www.linkedin.com/company/hum-ai)
2. [hum.ai Website](https://www.hum.ai/)
3. [hum.ai Careers Page (Ashby)](https://jobs.ashbyhq.com/hum-ai/)
4. [Startuply.vc - hum.ai Profile](https://startuply.vc/startup/hum-ai-cb0xk6)
5. [RemoteOtter - hum.ai Jobs](https://remoteotter.com/company/hum-ai)
6. [Built In - hum.ai Company Profile](https://builtin.com/company/humai-0)

## Other roles at hum.ai

- [AI Researcher](https://feeny.ai/job/ai-researcher-hum-ai-san-francisco-h1qe1vr3dhhg) — San Francisco, CA
- [Senior Machine Learning Engineer](https://feeny.ai/job/senior-machine-learning-engineer-checkout-com-london-362vyt6j2yca) — London, United Kingdom
- [Senior Machine Learning Engineer](https://feeny.ai/job/senior-machine-learning-engineer-fullscript-ottawa-z5sfy69ade9r) — Ottawa, Canada
- [Senior Machine Learning Engineer](https://feeny.ai/job/senior-machine-learning-engineer-samsara-united-states-8ar99crntrtm) — United States
- [Senior Machine Learning Engineer](https://feeny.ai/job/senior-machine-learning-engineer-checkr-san-francisco-california-s61j722jabj1) — San Francisco California, United States
- [Senior Machine Learning Engineer](https://feeny.ai/job/senior-machine-learning-engineer-caronsale-brazil-cnrnkp0rxh43) — Brazil
- [Senior Machine Learning Engineer](https://feeny.ai/job/senior-machine-learning-engineer-wpp-copenhagen-pa1gy9jxxx43) — Copenhagen, Denmark
- [Senior Machine Learning Engineer](https://feeny.ai/job/senior-machine-learning-engineer-kard-financial-inc-remote-nxr7e5eadr6a)
- [Senior Machine Learning Engineer](https://feeny.ai/job/senior-machine-learning-engineer-xero-ontario-1gbmfz62vsqp) — Ontario, Canada
- [Senior Machine Learning Engineer](https://feeny.ai/job/senior-machine-learning-engineer-attain-chicago-nann4kfj8c22) — Chicago, IL