Note: The job is a remote job and is open to candidates in USA. Atlassian is building agentic AI integration products, including the Rovo MCP Server and agent-to-agent integrations, to bring advanced AI capabilities into its ecosystem. The Senior ML Performance Engineer will design and optimize MCP servers, agent-facing APIs, AI-powered user experiences, semantic retrieval systems, and AI/ML quality programs while improving performance, reliability, security, and observability across enterprise systems.
Responsibilities
- 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
Skills
- 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
- 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
Benefits
- Atlassians can choose where they work – whether in an office, from home, or a combination of the two.
- Interviews and onboarding are conducted virtually.
- This role may also be eligible for benefits, bonuses, commissions, and equity.
- 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.
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