---
title: 'Senior Machine Learning Engineer at hum.ai'
canonical: 'https://feeny.ai/job/senior-machine-learning-engineer-hum-ai-remote-53h4tpxdgwe6'
type: 'job'
last_seen: '2026-10-28'
---

# Senior Machine Learning Engineer at hum.ai

- **Company:** hum.ai
- **Location:** Remote
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-10-26
- **Last confirmed live:** 2026-10-28
- **Apply:** https://jobs.ashbyhq.com/hum-ai/7ae548df-1ca0-4a90-9bf9-df5bb3f703d1

## Job description

Senior Machine Learning Engineer Location: San Francisco or remote · Full-time

## About us

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.

## The role

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.

## What you'll do

- 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.

## Requirements

- 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.

Bonus

- 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.

Why us?

- 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.

## 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)

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