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Atlassian

Senior Machine Learning System Engineer

Reposted 4 Days Ago
In-Office or Remote
Hiring Remotely in 10003, New York, NY, USA
149K-235K Annually
Senior level
In-Office or Remote
Hiring Remotely in 10003, New York, NY, USA
149K-235K Annually
Senior level
Design, build, and operate scalable, low-latency search and retrieval systems powering Atlassian products. Develop and serve production ML models (neural rankers, embeddings, rerankers) using PyTorch and Triton. Implement vector indexing and RAG/agentic retrieval pipelines, ensure observability and cost optimization, manage on-call/runbooks, and collaborate cross-functionally while mentoring junior engineers.
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Engineering | Seattle, United States | Remote, Remote | San Francisco, United States | Austin, United States | New York, United States
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.
As a senior Machine Learning Systems Engineer on the Search Platform team, you will own and drive the design, development, and production deployment of machine learning systems that power search experiences across Atlassian's product suite, including Jira, Confluence, and Rovo.
Search Platform EngineeringDesign and implement scalable search serving infrastructure, including retrieval pipelines, vector indexing systems, and embedding-based semantic search. Own end-to-end delivery of ML components from experimentation through production rollout across multiple regions and tenants. Contribute to the architecture of high-throughput, low-latency search systems that meet strict SLO targets for availability, latency, and relevance quality.
ML Model Development & ServingBuild and maintain production ML models including neural rankers, embedding models, and reranking systems. Integrate models into serving infrastructure using frameworks such as Triton and PyTorch, ensuring reliability, scalability, and cost efficiency. Collaborate with ML researchers to translate experimental models into production-grade systems with robust monitoring and evaluation harnesses.
Agentic Search & RetrievalDesign retrieval systems purpose-built for agentic and RAG (Retrieval-Augmented Generation) use cases, including personalized indexes, grounding pipelines, and multi-step retrieval workflows. Partner with Rovo and AI platform teams to evolve search infrastructure as a foundational layer for AI agents, ensuring retrieval quality, freshness, and relevance at scale.
Operational Excellence & Cost DisciplineDrive observability, monitoring, and incident response for search serving systems. Apply FinOps principles to identify and execute cost optimization opportunities across vector search infrastructure and ML serving fleets. Maintain production health through rigorous on-call practices, runbook development, and proactive capacity planning.
Cross-Functional CollaborationWork closely with engineering leads, product managers, and platform stakeholders to define technical roadmaps and deliver against team OKRs. Mentor junior engineers, contribute to design reviews, and champion engineering best practices across the team.
At Atlassian, we strive to design equitable, explainable, and competitive compensation programs. We follow consistent hiring practices and account for each candidate's skills, knowledge, and experience when setting base pay within the range.
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Atlassian New York, New York, USA Office

Atlassian believes the future of work is distributed and offers our people the flexibility to help them do what’s important to them. And with few exceptions, we hire people anywhere we have a legal entity as long as they have eligible work rights and sufficient team time zone overlap.

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