Senior Machine Learning System Engineer

Atlassian•Austin, TX

41d•$149,400 - $235,000•Remote

About The Position

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.

Requirements

Experience in designing and implementing scalable search serving infrastructure, including retrieval pipelines, vector indexing systems, and embedding-based semantic search.
Experience with end-to-end delivery of ML components from experimentation through production rollout across multiple regions and tenants.
Experience contributing to the architecture of high-throughput, low-latency search systems that meet strict SLO targets for availability, latency, and relevance quality.
Experience building and maintaining production ML models including neural rankers, embedding models, and reranking systems.
Experience integrating models into serving infrastructure using frameworks such as Triton and PyTorch, ensuring reliability, scalability, and cost efficiency.
Experience collaborating with ML researchers to translate experimental models into production-grade systems with robust monitoring and evaluation harnesses.
Experience designing retrieval systems purpose-built for agentic and RAG (Retrieval-Augmented Generation) use cases, including personalized indexes, grounding pipelines, and multi-step retrieval workflows.
Experience partnering 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.
Experience driving observability, monitoring, and incident response for search serving systems.
Experience applying FinOps principles to identify and execute cost optimization opportunities across vector search infrastructure and ML serving fleets.
Experience maintaining production health through rigorous on-call practices, runbook development, and proactive capacity planning.
Experience working closely with engineering leads, product managers, and platform stakeholders to define technical roadmaps and deliver against team OKRs.
Experience mentoring junior engineers, contributing to design reviews, and championing engineering best practices across the team.

Responsibilities

Design 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.
Build 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.
Design 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.
Drive 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.
Work 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.