Note: The job is a remote job and is open to candidates in USA. Plane is building an open, adaptable work management platform designed for collaboration between humans and AI agents. The Software Engineer, AI Application will build production agent workflows, MCP servers, retrieval systems, evaluation tooling, and customer-facing agent interfaces using Python, while partnering with engineering and product teams to ship reliable AI capabilities.
Responsibilities
- 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
Skills
- • 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-deterministic system and using it to make a ship or no-ship call
- • Examples of AI features you shipped to real users rather than to demos or notebooks
- • You have built or published an MCP server
- • You have run agents against production data with a real permission model behind them
- • You have worked on developer tools, project management, or another product with a complex domain model
- • You have contributed to open source. Plane is open source, and you will see your work in public
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