Senior Software Engineer, AI for the Planet
$126,000–$189,000 year
On-siteSeattle, Washington, United States
Job Summary
Design and ship full-stack products integrating state-of-the-art ML models for maritime conservation and climate solutions, managing end-to-end lifecycles from requirements to production deployment. Own production systems including on-call rotations, troubleshooting incidents via logs and metrics, and optimizing distributed training across thousands of GPUs for satellite imagery analysis. Collaborate directly with researchers and external partners to iterate quickly on agentic workflows and multi-tenant agent orchestration platforms, ensuring clear communication of AI outputs to non-technical users. Work primarily in our Seattle office with regular travel to meet users, focusing on building scalable infrastructure for real-time vessel detection and custom model fine-tuning.
Required Qualifications
- 5+ years of professional software engineering experience in industry
- 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
- 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
- Ownership over production systems
- You write clear technical plans, give and receive feedback well, constantly prioritize, and can guide stakeholders through the details
- 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
Desired 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
- 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
- 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
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