Senior ML 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 Atlassians 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.

At Atlassian we strive to design equitable explainable and competitive compensation programs. We follow consistent hiring practices and account for each candidates skills knowledge and experience when setting base pay within the range.

Please visit for more information on which locations are included in each of our geographic pay zones. However please confirm the zone for your specific location with your recruiter.

This role may also be eligible for benefits bonuses commissions and equity.

In The United States we have three geographic pay zones. For this role our current base pay ranges for new hires in each zone are:

Zone A: $180000 - $235000

Zone B: $162000 - $211500

Zone C: $149400 - $195050

• 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.