{"id":1590788,"url":"https://alion.io/job/atlassian-senior-machine-learning-engineer-2","title":"Senior machine learning engineer","company":{"id":182,"name":"Atlassian","domain":"atlassian.com","url":"https://alion.io/company/atlassian","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"iCIMS","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Austin, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":206100,"max":269075,"currency":"USD","period":"year","gross":null,"usd_annual":269075},"salary_estimate":null,"experience_years_min":12,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Databricks","optional":false},{"name":"Fine-tuning","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Post-training","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"SFT","optional":false},{"name":"Spark","optional":false},{"name":"Tool Use","optional":false}],"status":"live","first_seen_at":"2026-10-01T04:02:00Z","employer_posted_date":null,"last_verified_at":"2026-10-01T04:02:00Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"Overview\nWorking at Atlassian\n 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.\nResponsibilities\n\nSenior Machine Learning Engineer - Agentic Search & Query Intelligence\n 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.\nYour future team\n 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.\nOur 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.\nWe 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.\nWhat You’ll Do\n\nDevelop 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.\nBuild and improve agentic search planners that turn complex requests into search strategies, select appropriate sources and tools, and adapt based on retrieved evidence.\nImprove model and agent behavior through prompt development, model selection, training data improvements, and fine-tuning where appropriate.\nOwn projects from problem definition and prototyping through experimentation, production deployment, and ongoing measurement.\nBuild 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.\nBalance search quality with latency, inference cost, and reliability, building systems that operate effectively within enterprise permissions and data boundaries.\nCollaborate with search platform, relevance, Rovo Chat, and other AI teams to integrate query intelligence and agentic search capabilities into customer experiences.\nContribute to technical design and code reviews, mentor junior engineers, and share learnings that strengthen the team’s engineering and ML practices.\nYour background\n\nOn the first day, we’ll expect you to have\n\nA bachelor’s or master’s degree in Computer Science or a related field, or equivalent practical experience.\n4+ years of relevant industry experience in machine learning, with experience delivering ML capabilities into production.\nStrong Python programming skills and the ability to write reliable, maintainable, production-quality code.\nExperience in one or more of natural language processing, information retrieval, search relevance, or LLM applications.\nExperience designing experiments, building evaluation datasets, analyzing model behavior, and using evidence to guide improvements.\nAn understanding of the ML development lifecycle, from data preparation and modeling to deployment, monitoring, and iteration.\nThe ability to take ownership of ambiguous problems, make practical technical tradeoffs, and communicate clearly with engineering and product partners.\nIt’s Great, But Not Required, If You Have\n\nExperience building AI agents, tool-use workflows, multi-step search systems, or retrieval-augmented generation applications.\nExperience with query understanding, semantic or hybrid retrieval, ranking, personalization, or context-aware search.\nExperience with LLM fine-tuning or post-training, including supervised fine-tuning, preference optimization, or reinforcement learning.\nExperience developing evaluation approaches for agents, including trajectory analysis, human evaluation, or model-based judging.\nExperience with distributed data processing and cloud ML environments such as Spark, AWS, or Databricks.\nCompensation\n 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.\nPlease visit go.atlassian.com/payzones for more information on which locations are included in each of our geographic pay zones. However, please confirm the zone for your specific location with your recruiter.\nThis role may also be eligible for benefits, bonuses, commissions, and equity.\nPay Ranges\n\nRole\n In The United States, we have three geographic pay zones. For this role, our current base pay ranges for new hires in each zone are:\nZone A: $206,100 - $269,075\nZone B: $185,490 - $242,168\nZone C: $171,063 - $223,332\nQualifications\n\nBenefits & Perks\n Atlassian offers a wide range of perks and benefits designed to support you, your family and to help you engage with your local community. Our offerings include health and wellbeing resources, paid volunteer days, and so much more. To learn more, visit go.atlassian.com/perksandbenefits\n.\n\nAbout Atlassian\n At Atlassian, we're motivated by a common goal: to unleash the potential of every team. Our software products help teams all over the planet and our solutions are designed for all types of work. Team collaboration through our tools makes what may be impossible alone, possible together.\nWe believe that the unique contributions of all Atlassians create our success. To ensure that our products and culture continue to incorporate everyone's perspectives and experience, we never discriminate based on race, religion, national origin, gender identity or expression, sexual orientation, age, or marital, veteran, or disability status. All your information will be kept confidential according to EEO guidelines.\nTo provide you the best experience, we can support with accommodations or adjustments at any stage of the recruitment process. Simply inform our Recruitment team during your conversation with them.\nTo learn more about our culture and hiring process, visit go.atlassian.com/crh\n.\n In line with local law, identity verification (which may include use of biometric data) is a condition of employment with Atlassian for employment fraud purposes.","description_format":"text","description_chars":6803,"description_truncated":false,"requirements":{"experience_years_min":12,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Equity"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":true,"industries":["IT Service & Asset Management","Project & Work Management Software","Developer Tools","Intranet & Knowledge Management"],"lifecycle":[{"event":"open","at":"2026-10-01T16:03:12Z"}],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":23,"reasons":["seen:0","win:early"],"computed_at":"2026-10-02T03:21:33Z"},"pay":{"stated_usd_annual":269075,"is_top_pay":true},"html_url":"https://alion.io/job/atlassian-senior-machine-learning-engineer-2","json_url":"https://alion.io/job/atlassian-senior-machine-learning-engineer-2.json","meta":{"generated_at":"2026-10-02T03:21:33Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":4608,"day_limit":5000,"remaining_today":392,"minute_limit":60,"resets_at":"2026-10-03T00:00:00Z"}}}