Atlassian

1 week ago

Senior Machine Learning System Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 5 hours agoStatus: Live

Job Summary

Own the design, development, and production deployment of machine learning systems for search across Atlassian’s product suite, including Jira, Confluence, and Rovo. Build scalable search infrastructure with retrieval pipelines, vector indexing, and embedding-based semantic search. Develop and serve production ML models (rankers and rerankers) using Triton and PyTorch, and design retrieval for agentic and RAG workflows. Drive reliability, monitoring, incident response, and FinOps cost optimization while collaborating across product and platform teams.

Location: Seattle, San Francisco, Austin, New York

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Own the design, development, and production deployment of ML systems powering search experiences across Atlassian’s product suite.
•Design and implement scalable search serving infrastructure, including retrieval pipelines, vector indexing, and embedding-based semantic search.
•Develop and maintain production ML models (neural rankers, embedding models, reranking systems) and integrate them into serving using Triton and PyTorch.
•Design retrieval systems for agentic and RAG use cases and evolve search infrastructure as a foundational layer for AI agents with Rovo and AI platform teams.
•Drive observability, monitoring, incident response, and FinOps cost optimization for vector search infrastructure and ML serving fleets.

Pay and Benefits

Salary: USD 149,400 - 235,000 annually

Perks:Health InsurancePaid Volunteer

Key Requirements

•Own end-to-end delivery of ML components from experimentation through production rollout across multiple regions and tenants.
•Design scalable search serving infrastructure, including retrieval pipelines, vector indexing systems, and embedding-based semantic search with SLO targets.
•Build and maintain production ML models such as neural rankers, embedding models, and reranking systems, integrating them into serving infrastructure using Triton and PyTorch.
•Design retrieval systems for agentic and RAG use cases, including personalized indexes, grounding pipelines, and multi-step retrieval workflows.
•Drive observability, monitoring, incident response, runbook development, and proactive capacity planning, applying FinOps principles for cost optimization.

Experience:Machine learningAI agentsSearchRAGVector search

Skills:Cross-functional collaborationMentorshipDesign reviewsOperational excellenceIncident response

Tech Stack:TritonPyTorchRAGVector indexingEmbeddingsNeural rankersSemantic search

Company Brief

Atlassian

Atlassian develops collaboration and workflow software—Jira, Confluence, Trello, Loom and more—used by teams for project management, software development, and IT service management across enterprises worldwide.

Industry: Developer Tools

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Sydney, Australia

Founded: 2002

Glassdoor: 3.1

Website LinkedIn Glassdoor

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Atlassian

1 week ago

Senior Machine Learning System Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 5 hours agoStatus: Live

Job Summary

Location: Seattle, San Francisco, Austin, New York

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Own the design, development, and production deployment of ML systems powering search experiences across Atlassian’s product suite.
•Design and implement scalable search serving infrastructure, including retrieval pipelines, vector indexing, and embedding-based semantic search.
•Develop and maintain production ML models (neural rankers, embedding models, reranking systems) and integrate them into serving using Triton and PyTorch.
•Design retrieval systems for agentic and RAG use cases and evolve search infrastructure as a foundational layer for AI agents with Rovo and AI platform teams.
•Drive observability, monitoring, incident response, and FinOps cost optimization for vector search infrastructure and ML serving fleets.

Pay and Benefits

Salary: USD 149,400 - 235,000 annually

Perks:Health InsurancePaid Volunteer

Key Requirements

•Own end-to-end delivery of ML components from experimentation through production rollout across multiple regions and tenants.
•Design scalable search serving infrastructure, including retrieval pipelines, vector indexing systems, and embedding-based semantic search with SLO targets.
•Build and maintain production ML models such as neural rankers, embedding models, and reranking systems, integrating them into serving infrastructure using Triton and PyTorch.
•Design retrieval systems for agentic and RAG use cases, including personalized indexes, grounding pipelines, and multi-step retrieval workflows.
•Drive observability, monitoring, incident response, runbook development, and proactive capacity planning, applying FinOps principles for cost optimization.

Experience:Machine learningAI agentsSearchRAGVector search

Skills:Cross-functional collaborationMentorshipDesign reviewsOperational excellenceIncident response

Tech Stack:TritonPyTorchRAGVector indexingEmbeddingsNeural rankersSemantic search

Company Brief

Atlassian

Industry: Developer Tools

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Sydney, Australia

Founded: 2002

Glassdoor: 3.1

Website LinkedIn Glassdoor