About this role
Senior ML Performance Engineer
Be the backbone of Atlassian’s Agentic AI Integration Products: The Agentic AI Integrations team is responsible for the industry-leading Rovo MCP Server, Agent to Agent integrations as well as on the mission to catapult Atlassian value by leveraging cutting-edge AI capabilities like Claude Skills, ChatGPT/Claude Apps etc., essentially we will be working on anything and everything with AI integrations into the Atlassian ecosystem.
Knack to work on bleeding-edge AI technologies: Passionate to explore and learn AI transformative technologies and quickly pivot from prototyping new initiatives to building highly-scalable enterprise-grade AI products that will be used by 1000s of developers and enterprise users.
ML performance, quality, and systems acumen-ship: Experience in tuning MCP or agent-facing servers for latency, reliability, token efficiency, and tool-selection quality; including dynamic tool discovery, context and response optimization, observability, automated evals, and semantic retrieval using embeddings, vector search, hybrid ranking, and reranking.
Design, build, and evolve MCP servers, tools, and agent-facing APIs with concise schemas, predictable errors, safe mutations, and clear outcome-oriented contracts.
Develop accessible, responsive, and performant React and TypeScript experiences that make agent capabilities, MCP tools, and A2A interactions easy to discover, configure, and use.
Build reusable components, design-system patterns, and frontend architecture that support consistent, scalable user experiences across AI-powered products.
Integrate GraphQL and REST APIs, SDKs, streaming responses, and real-time data into reliable, user-friendly AI workflows.
Optimize token and context efficiency through dynamic tool discovery, lazy loading, bounded responses, pagination, selective field retrieval, caching, and reduced tool-call loops.
Improve end-to-end performance and reliability across front-end clients, gateways, MCP servers, search services, and downstream product systems through observability, tracing, SLOs, and production diagnostics.
Build semantic retrieval capabilities using embeddings, chunking, vector indexes, hybrid search, metadata and permission filte