Senior Software Engineer

Senior Software Engineer, Agent Platform (AI for the Planet)Seattle, WAOur base salary range is $146,880 - $220,320, and in addition we have generous bonus plans to provide a competitive compensation package.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 a few products:EarthRanger is a software solution from AI2 that helps conservationists make informed operational decisions. By integrating real-time data from hardware sensors, field personnel, satellite imagery, and other sources, we help prevent the poaching of endangered species, fight deforestation, promote human-wildlife coexistence, and advocate for legal protection of wildlife. From iconic national parks throughout Africa to species-specific conservation programs in the arctic, today over 1000 conservation missions in 100 countries are using EarthRanger to protect animals and their habitats.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.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.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 BelieveThe 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.Your Next Challenge:We are building an agent platform for conservation groups, government agencies, and research organizations that want to run AI agents against their own data. They know their domain and they have the data. We give them the application infrastructure: the runtime, the APIs, the tooling, and the operational support to get an agent from a prototype to something they can rely on.You would build that foundation. That means the APIs those teams call, the SDK and CLI they install, the docs they read, the traces they use when something breaks, and the evaluation tooling that tells them whether their agent is any good. We want a developer outside our team to get from an idea to a running, observable agent without needing us on a call.You would also be one of those developers. We build and operate our own agents on this platform for real users, and you would spend real time doing that too. It is how we find the problems before our customers do. The work spans both sides of the platform. You would build the services that run agents, and you would build the interfaces other engineers use to reach them.Design and evolve the platform APIs every agent depends on, covering registration, auth, secrets, deployment, runtime, events, discovery, and evaluation. Keep them stable, documented, and pleasant to use.Build the observability layer that shows developers what their agents are actually doing: logs, conversation history, execution traces, metrics, and failure reporting.Make evaluation a first-class part of the platform, so developers can define and run evals, compare versions, track quality over time, and see regressions early.Build and operate the services underneath all of it: identity and authorization, runtime orchestration, deployment, event routing, and the agent registry.Build agents yourself and run them in production, including prompt and tool design, quality and cost tuning, and keeping them healthy once real users depend on them.Watch for patterns that keep coming up in customer work and turn them into real platform features.Own the developer experience end to end: onboarding, quickstarts, SDKs, CLI tooling, example agents, API documentation, and a local development loop that works.You would work closely with the front-end team building the user-facing product on your APIs, and with product management on where platform investment pays off most. You would also spend time with customers directly, watching them build, hearing where they get stuck, and bringing that back into the platform.What You'll Need:8+ years of professional software engineering experience.Range across web services, distributed systems, data pipelines, and cloud tooling.A track record of shipping products to external users and owning them in production.Opinions about API design, and a preference for simple designs over clever ones.Willingness to write documentation as part of the work rather than after it.Fluency with modern AI tools in your own workflow.A continuous learner who adapts quickly to the rapidly evolving landscape of LLMs and agent architectures.Clear technical communication with engineers and non-engineersNice to Have:Experience building developer platforms, SDKs, or public APIs for outside consumers.Hands-on experience shipping LLM agents to production, including tool use, orchestration, sandboxing, and cost and latency tuning.Experience building evaluation or experimentation systems for nondeterministic software.Small or growth-stage company experience.Public technical work such as open source contributions, writing, or side projects.Willingness to travel on occasion.How We WorkThe mission is the point. We stay close to our users, iterate in the open, and ship high-quality code quickly. We hire for curiosity and expect ideas to be challenged in open discussion.Physical Demands and Work Environment:Must be able to remain in a stationary position for long periods of time.The ability to communicate information and ideas so others will understand. Must be able to exchange accurate information in these situations.The ability to observe details at close range.Can work under deadlines.A Little More About Ai2:Ai