Senior Machine Learning System Engineer

Atlassian is a company dedicated to unleashing the potential of every team through their software products. They are seeking a Senior Machine Learning System Engineer to design, develop, and deploy machine learning systems that enhance search experiences across their product suite.

Responsibilities:

• 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
• 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

Requirements:

• Experience in designing and implementing scalable search serving infrastructure, including retrieval pipelines, vector indexing systems, and embedding-based semantic search
• Proven ability to own 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 in building and maintaining production ML models including neural rankers, embedding models, and reranking systems
• Proficiency in integrating models into serving infrastructure using frameworks such as Triton and PyTorch, ensuring reliability, scalability, and cost efficiency
• Ability to collaborate with ML researchers to translate experimental models into production-grade systems with robust monitoring and evaluation harnesses
• Experience in designing retrieval systems purpose-built for agentic and RAG (Retrieval-Augmented Generation) use cases, including personalized indexes, grounding pipelines, and multi-step retrieval workflows
• Ability to 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
• Experience in driving observability, monitoring, and incident response for search serving systems
• Knowledge of applying FinOps principles to identify and execute cost optimization opportunities across vector search infrastructure and ML serving fleets
• Experience in maintaining production health through rigorous on-call practices, runbook development, and proactive capacity planning
• Ability to work 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