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# Senior machine learning engineer

- **Company:** Atlassian
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $206,100.0 - $269,075.0
- **Contract:** Permanent contract
- **Skills:** Training Data, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Command-Line Interface, Code Review, Data Cleansing, Software Debugging, Programming Tools, Distributed Computing Environment, Information Retrieval, Python (Programming Language), Machine Learning, Retrieval-Augmented Generation, Transfer Learning, Large Language Models, Apache Spark, Agentic-AI, Information Technology, Model Context Protocol, Databricks
- **Published:** November 6, 2026
- **Apply:** https://www.juju.com/job/7_C971A2677101A5A65AA4DAC2EE4DB281

## About the Role

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 haveA 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 haveExperience 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.

## Description

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 doBuild 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