Atlassian provides tools to help every team unleash their full potential.
The Role
Develop and deploy machine learning and LLM capabilities for agentic search, query understanding, search planning, and retrieval. Build evaluation datasets, improve model behavior through experimentation and fine-tuning, and optimize quality, latency, cost, and reliability. Collaborate across search, product, infrastructure, and AI teams while owning projects through production and mentoring engineers.
Summary Generated by Built In
Engineering | Mountain View, United States or Remote | Remote, Remote | San Francisco, United States | Seattle, United States | Austin, United States | New York, United States
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.
Senior Machine Learning Engineer - Agentic Search
Atlassian is seeking a Senior Machine Learning Engineer to join our Agentic Search team. You'll build agentic search systems that help people and AI agents discover relevant enterprise knowledge, reason through complex questions, and accomplish tasks across products and tools.
Your future team
Our team is part of Search Relevance at Atlassian. We bring enterprise search into the places where people and agents work, including Atlassian products, third-party AI assistants, and developer workflows.
We build systems that interpret intent, plan searches, use tools, and adapt as new evidence becomes available. Rigorous evaluation guides our development: we benchmark realistic tasks, investigate failures, and use what we learn to improve the full search experience-from agent behavior to the interfaces and information we provide.
We combine applied research with production engineering, working closely with product, search infrastructure, modeling, and evaluation teams. We use AI tools throughout our daily work to prototype, build, evaluate, and learn, and continually evolve our methods as the technology advances.
What you'll do
Your background
On the first day, we'll expect you to have
It's great, but not required, if you have
At Atlassian, we strive to design equitable, explainable, and competitive compensation programs. We follow consistent hiring practices and account for each candidate's skills, knowledge, and experience when setting base pay within the range.
Please visit
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.
Senior Machine Learning Engineer - Agentic Search
Atlassian is seeking a Senior Machine Learning Engineer to join our Agentic Search team. You'll build agentic search systems that help people and AI agents discover relevant enterprise knowledge, reason through complex questions, and accomplish tasks across products and tools.
Your future team
Our team is part of Search Relevance at Atlassian. We bring enterprise search into the places where people and agents work, including Atlassian products, third-party AI assistants, and developer workflows.
We build systems that interpret intent, plan searches, use tools, and adapt as new evidence becomes available. Rigorous evaluation guides our development: we benchmark realistic tasks, investigate failures, and use what we learn to improve the full search experience-from agent behavior to the interfaces and information we provide.
We combine applied research with production engineering, working closely with product, search infrastructure, modeling, and evaluation teams. We use AI tools throughout our daily work to prototype, build, evaluate, and learn, and continually evolve our methods as the technology advances.
What you'll do
- Build and deliver agentic search capabilities for AI assistants and developer workflows through APIs, command-line tools, and protocols such as the Model Context Protocol (MCP).
- Develop reproducible benchmarks and evaluation tools that reflect realistic tasks across models and agent environments. Enable trustworthy performance comparisons, make failures easier to diagnose, and accelerate experimentation.
- Use evaluation results, production signals, and developer feedback to improve the full search system-from search strategies and model behavior to tool interfaces, input and output schemas, and context selection.
- Build agentic search systems that interpret user intent, plan and execute searches, select appropriate sources and tools, and adapt based on retrieved evidence.
- Explore and apply advances in LLMs and agent systems, including prompting, model selection, training data improvements, and fine-tuning where appropriate. Use evidence to decide which approaches to bring into production.
- Own projects from problem definition and technical design through experimentation, deployment, and ongoing measurement, building reliable systems that respect enterprise permissions and data boundaries.
- Collaborate across product, search, and AI teams to shape technical direction and integrate agentic search into customer experiences.
- Contribute to technical design and code reviews, mentor engineers, and help the team evolve its engineering practices, including effective use of AI tools.
Your background
On the first day, we'll expect you to have
- A bachelor's or master's degree in Computer Science or a related field, or equivalent practical experience.
- 4+ years of relevant industry experience in machine learning, including delivering ML capabilities into production.
- Strong Python programming skills and the ability to build reliable, maintainable production systems.
- Experience in one or more of LLM applications, AI agents, information retrieval, search relevance, or natural language processing.
- Experience designing experiments, building evaluation datasets, and analyzing model and system behavior to guide improvements.
- Regular use of AI tools in your daily engineering workflow, such as coding, prototyping, debugging, or experimentation, with the judgment to validate their outputs and take ownership of the results.
- An understanding of the ML development lifecycle, from data preparation and modeling to deployment, monitoring, and iteration.
- The ability to lead ambiguous technical projects, learn new approaches quickly, make practical tradeoffs, and communicate clearly with engineering and product partners.
It's great, but not required, if you have
- Experience building agents that use tools, multi-step search systems, or retrieval-augmented generation applications.
- Experience designing APIs, developer tools, or interfaces that make search and knowledge accessible to AI agents, including MCP.
- Experience developing agent evaluations or reproducible benchmarks, including trajectory analysis, human evaluation, or model-based judging.
- Experience with semantic or hybrid retrieval, ranking, or context-aware search.
- Experience with LLM fine-tuning or post-training, including supervised fine-tuning, preference optimization, or reinforcement learning.
- Experience with distributed data processing and cloud ML environments such as Spark, AWS, or Databricks.
At Atlassian, we strive to design equitable, explainable, and competitive compensation programs. We follow consistent hiring practices and account for each candidate's skills, knowledge, and experience when setting base pay within the range.
Please visit
Skills Required
- Bachelor’s or master’s degree in Computer Science or a related field, or equivalent practical experience
- 4+ years of relevant industry experience in machine learning
- Experience delivering machine learning capabilities into production
- Strong Python programming skills and ability to write reliable, maintainable, production-quality code
- Experience in natural language processing, information retrieval, search relevance, or LLM applications
- Experience designing experiments, building evaluation datasets, analyzing model behavior, and using evidence to guide improvements
- Understanding of the machine learning development lifecycle, including data preparation, modeling, deployment, monitoring, and iteration
- Ability to take ownership of ambiguous problems, make practical technical tradeoffs, and communicate clearly with engineering and product partners
- Experience building AI agents, tool-use workflows, multi-step search systems, or retrieval-augmented generation applications
- Experience with query understanding, semantic or hybrid retrieval, ranking, personalization, or context-aware search
- Experience with LLM fine-tuning or post-training, including supervised fine-tuning, preference optimization, or reinforcement learning
- Experience developing evaluation approaches for agents, including trajectory analysis, human evaluation, or model-based judging
- Experience with distributed data processing and cloud ML environments such as Spark, AWS, or Databricks
What the Team is Saying
Tiffany
Engineering Senior Associate, United States
Gautam
Software Engineer, United States
Tygrr
Product Manager, United States
Ashleigh
Frontend Engineer, United States
Jensen
Senior Frontend Software Engineer, United States
Javier
Senior Solutions Engineer, United States
Atlassian Compensation & Benefits Highlights
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Parental & Family Support — Parental leave spans 26 weeks for birthing parents and 20 weeks for non-birthing parents, alongside inclusive family-formation benefits such as fertility treatment, adoption, and surrogacy. Additional supports include menopause resources and caregiving-related programs.
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Retirement Support — Retirement programs include a U.S. 401(k) company match with immediate vesting and multiple contribution options. These are complemented by other financial wellbeing tools like HSAs/FSAs.
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Healthcare Strength — Medical coverage is paired with mental-health resources, disability and life insurance, and options such as HSA/FSA. Descriptions also point to solid plan choices with access to counseling, coaching, and 24/7 support.
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The Company
What We Do
Atlassian creates teamwork solutions for high-performing teams. Our portfolio of collaboration and work management software products includes Jira, Confluence, Trello, Loom and Rovo. More than 300,000 businesses worldwide rely on Atlassian’s technology, including 80 percent of Fortune 500 companies. Our solutions support various business teams and they help organizations plan, track, and deliver their biggest ideas together.
Why Work With Us
At Atlassian, we believe we can accomplish so much more together than apart — which is why everything from our tooling — to our distributed workforce — to how our teams are structured is rooted in collaboration. Come join us and help unleash the potential of every team.
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