Senior Software Engineer, AI for the Planet

Persons in these roles are expected to work from our offices in Seattle. On-site requirements vary based on position and team. If you have questions about on-site work arrangements for this role, please ask your recruiter.

Our base salary range is $126,000 - $189,000 and in addition we have generous bonus plans to provide a competitive compensation package.

Who We Are:

We are a small engineering team at the Allen Institute for AI working on AI for the Planet. We're working on maritime conservation, food security, disaster resilience, and climate solutions with some of the most impactful organizations on the planet. We work very closely alongside a ML research team and our Product & Partnerships teams, focused on building products that support our environmental and high-impact users.

Today, our team works on two products:

Skylight uses AI to detect illegal, unreported, and unregulated fishing in real time. Governments, enforcement agencies, and conservation organizations in 95+ countries use it to protect their waters. Our advanced AI-powered platform delivers real-time vessel detections and actionable insights that empower enforcement agencies globally to protect marine ecosystems. Read more at https://allenai.org/skylight. 

OlmoEarth is an open, end-to-end platform built around our family of foundation models for Earth observation. The OlmoEarth Platform enables our users to create custom fine-tuned models to detect and classify novel geospatial features. The platform handles the full loop: imagery acquisition from Sentinel-1, Sentinel-2, and Landsat; annotation; distributed training and inference; and a viewer so the outputs are usable by people who aren't ML experts. Partners today include NASA JPL (wildfire risk), IFPRI (crop mapping in Kenya), Global Mangrove Watch, and the Amazon Conservation Alliance. Read more at https://allenai.org/olmoearth. 

If you're the kind of engineer who gets energized by building technology that helps protect oceans, forests, and the climate, who wants to move fast, work across disciplines, and see your code have real-world impact, this is for you.

What We Believe:

The mission is the point. We're building AI for the planet: environmental conservation, food security, climate. If it’s important to you to work on problems with a positive impact on our planet and the world, you’re in the right place. 

The engineer closest to the user makes the best decisions. We put weight on talking to users, sitting with partnerships, and working side by side with researchers. You can't ship the right thing if you don't understand who you're shipping it for. This engineering team travels regularly to meet with users. 

Iterate small. Our users are tackling huge problems: illegal fishing, food security, climate resilience. They need tools that genuinely help. We believe the fastest way to build those tools is to design and build alongside them as partners: ship something functional, learn from how they use it, and iterate from there. Keeping users in the loop is how we build a better product, faster.

We ship high-quality code quickly, and we learn fast from mistakes. We hold a high bar for what we put into production, but we also move with urgency. When something breaks, we focus on understanding the system, not blaming individuals. Failures are signals that help us strengthen the layers that protect our users.

In-person matters. A lot of the best work on this team happens in unscheduled hallway conversations between engineering, research, and partnerships. We're in the office most days because that's where the team is at its best.

We hire for curiosity. The technologies we use will change over the years, and the engineers who do well here are the ones who enjoy learning new things, not the ones who've memorized a particular toolkit.

Ideas get better when they're challenged. We make decisions by talking them through - asking questions, pushing back when something doesn't quite add up, and being open to changing our minds. Everyone here is still learning, and we like it that way.

Who You Are: 

We're looking for a strong builder. Someone with deep experience shipping high-quality, scalable, full-stack products that integrate state-of-the-art ML models. Someone who wants to grow alongside a team working at the forefront of applied AI, on a product that exists to do good in the world. A great candidate is someone who thoughtfully reflects on our internal processes and is comfortable pushing for change.

Required Qualifications:

• 5+ years of professional software engineering experience in industry. Internships, graduate school, and research positions are valuable but do not count toward this.
• Experience working as a generalist software engineer across multiple parts of the stack and product lifecycle. Strong foundations in web applications, data pipelines, distributed systems, and modern cloud tooling. The specific frameworks matter less than demonstrated ability to pick up new technologies as the field evolves.
• You're using modern AI tooling (e.g. Claude Code, agentic workflows) to move faster and rethink how engineering gets done.
• A track record of taking software products end to end. Shaping requirements, designing architecture, shipping to external users, and continuing to develop them over time based on user feedback.
• Ownership overproduction systems. Including taking on-call rotations, troubleshooting production issues, and digging into logs, metrics, and code to develop real, actionable insight when something needs attention.
• You write clear technical plans, give and receive feedback well, constantly prioritize, and can guide stakeholders through the details.

Preferred qualifications:

• Experience at small or growth-stage companies, where you own outcomes end to end without heavy process scaffolding.
• Hands-on experience integrating machine learning models into production systems: deployment, monitoring, scaling real-time inference, and iteration. You collaborated directly with researchers to bring models out of a research context, and you built user-facing applications where AI outputs need to be communicated clearly to non-technical users. 
• You're opinionated about software engineering practice: coding patterns, breaking down work, code review, testing, build systems. You bring that judgment to the team while staying open to other perspectives.
• A demonstrated track record of technical depth and self-directed learning - for example: open-source contributions, technical writing, conference talks, sustained side projects.
• Open to occasional international travel to meet directly with the people using what we build.

Projects We’re Excited About:

To give you a better idea of the kinds of projects we work on, here are some examples of our current and past  projects:

• Automated model development: We're building the OlmoEarth Platform to enable users to go from raw tabular data to a fine-tuned, evaluated, production-ready computer vision model, without needing an ML engineer. The underlying infrastructure allows us to run jobs across thousands of parallel GPUs and terabytes of satellite imagery - covering continent-sized areas for fractions of a penny per square kilometer. We're also pushing into agentic approaches: agents that help with dataset discovery, preparation, and augmentation, and agents that explore model configurations and architectures to find the right setup for a given use case.
• Deploy multi-tenant agents: We are building a multi-tenant agent-orchestration platform to power Skylight's next generation of AI products - starting with Shippy, our maritime-domain-awareness agent. Every end user gets their own isolated sandbox: a per-user container stack with persistent GCS-backed state, a conversation history, and a hardened network boundary where the agent runtime can run free, in a secure environment. This platform will allow us to launch agentic-powered products without re-inventing the wheel every time. 
• Sentinel-2 vessel detections: We use the Sentinel-2 Satellites from the European Space Agency to detect locations of vessels globally in near-real-time. Our data-pipelines download imagery as soon as it’s available and run our state-of-the-art computer vision models to detect vessels and make t