Senior machine learning engineer

Atlassian•Austin, TX

4d•$171,063 - $269,075•Remote

About The Position

Atlassian is seeking a Senior Machine Learning Engineer to join our Query Intelligence team, working on Agentic Search and Search Relevance. You will build intelligent systems that help people and AI agents understand complex questions, discover relevant knowledge, and accomplish tasks across Atlassian products and connected tools. Our team is part of Search Relevance within Intelligence & Experience. We build the intelligence that connects what users and agents are trying to accomplish with the information they need. Our work spans query understanding, search planning, and agentic retrieval. We develop capabilities that interpret user intent, break complex requests into actionable searches, incorporate organizational context, and refine search strategies as new evidence becomes available. These capabilities support experiences across Rovo Search, Rovo Chat, and AI agents. We work closely with product, search infrastructure, modeling, and evaluation teams. We combine applied research with production engineering, using experimentation and customer feedback to improve search quality, reliability, and efficiency.

Requirements

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, with experience delivering ML capabilities into production.
Strong Python programming skills and the ability to write reliable, maintainable, production-quality code.
Experience in one or more of 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.
An understanding of the ML development lifecycle, from data preparation and modeling to deployment, monitoring, and iteration.
The ability to take ownership of ambiguous problems, make practical technical tradeoffs, and communicate clearly with engineering and product partners.

Nice To Haves

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.

Responsibilities

Develop machine learning and LLM capabilities for query intelligence, including intent understanding, query rewriting and decomposition, entity understanding, and translating natural language into structured search constraints.
Build and improve agentic search planners that turn complex requests into search strategies, select appropriate sources and tools, and adapt based on retrieved evidence.
Improve model and agent behavior through prompt development, model selection, training data improvements, and fine-tuning where appropriate.
Own projects from problem definition and prototyping through experimentation, production deployment, and ongoing measurement.
Build datasets and evaluation methods, partnering with evaluation teams to measure retrieval relevance, evidence coverage, task success, and grounding. Use offline analysis and online experiments to diagnose failures and validate improvements.
Balance search quality with latency, inference cost, and reliability, building systems that operate effectively within enterprise permissions and data boundaries.
Collaborate with search platform, relevance, Rovo Chat, and other AI teams to integrate query intelligence and agentic search capabilities into customer experiences.
Contribute to technical design and code reviews, mentor junior engineers, and share learnings that strengthen the team’s engineering and ML practices.