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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 teamOur 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 experiencefrom 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 systemfrom 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 backgroundOn 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.
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