Software Engineer, AI Application

About Plane Plane's mission is to build the infrastructure the world's work runs on. Every organization runs on three things: the projects it's driving, the knowledge it keeps, and the requests it fields. Plane brings all three into one open, adaptable platform: simple enough for any team to adopt, dependable enough for organizations to build on. And we are building it for a future where humans and AI agents do that work together. Plane began in public on GitHub at the end of 2022. Since then it has grown into a work management platform used by teams around the world: 55,000+ stars, 5,000+ forks, and a contributor community that reads our code and files our issues. Organizations run Plane as a managed Cloud service, on their own infrastructure, or inside fully isolated environments. Building in the open keeps us close to users and raises the standard for everything we ship. Plane is the #1 work infrastructure in aerospace, defense, financial services, and other regulated industries: organizations whose requirements for control, auditability, and data residency rule most software out. When the strictest buyers pick a system of record, that choice means something. Adoption is growing fastest on Plane Cloud and in sovereign clouds, deployments that keep everything inside a country's own borders and rules. Plane is backed by top investors and built across San Francisco, London, and Hyderabad. We work in tightly knit teams, stay close to users, and care about the visible product as much as the unglamorous details that make software dependable. People own problems end to end, and we add process only when it helps the work. Humans and agents We believe the next decade of work will be done by humans and AI agents together. Not agents replacing people, and not a chat window bolted onto software built for humans clicking around, but both working in one system of action, where an agent's work is as visible and as accountable as a person's. Most software treats AI as a feature. We treat agents as a kind of worker, and that changes what the system underneath has to be. Agents are only as good as the context they can see and the state they are allowed to change, so shared context, explicit state, durable history, and accountable action are not items on our roadmap. They are the product. Plane is built so that when an agent acts, the humans responsible can see what happened, why, and on whose authority, and the record survives. This is what the infrastructure is for: making the future where humans and agents work together useful, legible, and fully within the organization's control. Every role at Plane is some part of building that. About the role Plane is where work gets planned, tracked, and shipped. You will build the layer that lets agents participate in that work directly, reading context from a workspace, taking action inside it, and handing control back to a human at the right moment. This is product engineering, not research. You will write Python, design the retrieval and tool surfaces that agents depend on, and own what you ship once real customers are using it. What you'll do Build agent workflows that operate on Plane data, including planning loops, tool calling, multi-step execution, and recovery when a step fails. Design and ship MCP servers that expose Plane capabilities as tools, defining the schemas, permissions, and error semantics that make them safe for a model to call. Own the retrieval layer end to end, covering chunking strategy, embedding models, vector storage, hybrid search, and reranking. Build the agent interfaces that customers actually touch, including streaming output, intermediate step visibility, approval gates, and clean interruption. Establish the evaluation harness for everything you ship, defining datasets, regression suites, and the offline and online metrics that decide whether a change goes out. Instrument tracing, token cost, and latency across agent runs, and drive down the failure modes that show up in production. Design the permission and audit model for agent actions so an agent can only reach what the acting user can reach. Evaluate models, frameworks, and techniques as they land, and make a clear recommendation on what Plane should adopt and what it should skip. Partner with backend, frontend, and product engineers to take a working prototype to a shipped surface. What you'll bring Two or more years building production software, including at least one year shipping features backed by large language models. Strong Python and experience operating services in production, including debugging them under real traffic. Experience building agent systems that call tools, covering the orchestration, the failure handling, and the loop control that keeps them from running away. Working knowledge of retrieval systems, including embedding models, chunking tradeoffs, vector databases, and the retrieval quality problems that follow. Experience building an evaluation harness for a non-determi