Senior Performance Engineer

Working at Atlassian

Atlassians can choose where they work – whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity. Interviews and onboarding are conducted virtually, a part of being a distributed-first company.

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 filters, ranking, reranking, and freshness strategies.
- Define and operate AI/ML quality programs with JTBD-based evaluations, benchmark datasets, groundedness and relevance metrics, hallucination and bias detection, safety testing, and human feedback.
- Integrate automated evaluations into CI/CD and release gates to detect regressions across model, prompt, tool, and retrieval changes.
- Implement enterprise security and partner cross-functionally to deliver maintainable, well-tested AI integrations, including OAuth 2.1, tenant isolation, audit logging, prompt-injection defenses, and confirmation flows for high-impact actions.

- 7+ years of software engineering experience building and operating enterprise systems, APIs, or cloud-native products.
- Strong proficiency in TypeScript/JavaScript and modern front-end development with React; experience building accessible, responsive, and performant web applications.
- Hands-on experience designing or integrating MCP servers, tools, agent-facing APIs, or related context and agent frameworks.
- Demonstrated ability to apply distributed-systems principles, including concurrency, connection pooling, caching, retries, timeouts, backpressure, autoscaling, and load shedding.
- Experience measuring and improving latency, throughput, saturation, error rates, availability, token consumption, and end-to-end task cost.
- Practical experience with AI/ML evaluation and quality engineering, including benchmark design, groundedness, relevance, safety, hallucination detection, bias analysis, monitoring, and regression prevention.
- Knowledge of semantic search and retrieval systems, including embeddings, vector databases or indexes, hybrid retrieval, ranking, reranking, and permission-aware filtering.
- Experience integrating GraphQL, REST, JSON Schema, streaming APIs, SDKs, and event-driven systems into reliable product experiences.
- Proficiency in at least one additional systems or back-end language such as Python or Go, with strong testing and API design practices.
- Proven ability to lead cross-functional engineering initiatives, communicate clearly with technical and non-technical partners, and mentor other engineers.

Preferred Skills

- Familiarity with MCP architecture, A2A specification, and agent collaboration frameworks (e.g., MCP servers, UI clients, and adapters).
- Experience with observability, vector databases, and secure model-to-model communication.
- Background in enterprise integration patterns, API governance, or developer experience platforms.
- Contributions to open-source AI frameworks or standards development initiatives.

Why Join

- Work at the forefront of AI interoperability and system design.
- Influence emerging standards that define how agents and models communicate.

- • Collaborate with top engineers and  research partners building the next layer of enterprise AI infrastructure.