Senior ML Systems Engineer: Scalable Search Platform Remote

Atlassian; Austin (TX); On-site; USD 160,000 - 220,000; Full time

Benefits offered by this job

Health & wellbeing resources

Volunteer days

Community involvement

Job summary

Atlassian is seeking a Senior Machine Learning Systems Engineer for the Search Platform team. You will own the design, development, and production deployment of ML systems powering search across Jira, Confluence, and Rovo.

You will build embedding-based, low-latency retrieval pipelines and production models integrated with Triton and PyTorch. You will collaborate with ML researchers and platform teams, drive cross-region deployment, and mentor junior engineers, while focusing on observability,

Qualifications

• Proven experience designing and deploying production ML systems.
• Strong background in scalable search platforms and retrieval pipelines.
• Experience building models for ranking, embedding generation, and RAG use cases.

Responsibilities

• Own and drive the design, development, and production deployment of ML systems powering search across Jira, Confluence, and Rovo.
• Design scalable search serving infrastructure, including retrieval pipelines and embedding-based search.
• Build production ML models (neural rankers, embeddings, rerankers) and integrate with serving infra using PyTorch/Triton.
• Develop agentic search & retrieval workflows for RAG use cases with personalized indexes.
• Drive observability, monitoring, and FinOps-driven cost optimization for vector search infra.
• Collaborate with ML researchers and platform teams; mentor junior engineers.

Skills

ML systems design

Search infrastructure

Distributed systems

Python

PyTorch

Triton

Cost optimization

Tools

PyTorch

Triton

Vector search

Job description

Atlassian is seeking a Senior Machine Learning Systems Engineer for the Search Platform team. You will own the design, development, and production deployment of ML systems powering search across Jira, Confluence, and Rovo.

You will build embedding-based, low-latency retrieval pipelines and production models integrated with Triton and PyTorch. You will collaborate with ML researchers and platform teams, drive cross-region deployment, and mentor junior engineers, while focusing on observability,