[Remote] Senior Machine Learning System Engineer
Note: The job is a remote job and is open to candidates in USA. Atlassian is a company focused on unleashing the potential of every team through their software products. They are seeking a Senior Machine Learning System Engineer to lead the design and deployment of machine learning systems that enhance search functionalities 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
Skills
- 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
Benefits
- Health and wellbeing resources
- Paid volunteer days
Company Overview
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