{"id":4164598,"url":"https://alion.io/job/atlassian-senior-machine-learning-engineer-15","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":"Career site","truth_index":{"grade":"A","score":100,"open_postings":31,"ghost_share":0,"stale_share":0,"repost_share":0.065,"time_to_fill_p50_days":11,"computed_at":"2026-11-12T10:38:37Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Gdańsk, Poland"],"countries":["PL"],"hiring_countries":["PL"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Databricks","optional":false},{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Jira","optional":false},{"name":"LLM","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Semantic Search","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-11-12T13:11:32Z","employer_posted_date":"2026-11-12","last_verified_at":"2026-11-12T13:11:32Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Working at Atlassian\nHelp product teams turn customer feedback into better product decisions.\nWe are building AI capabilities for the Feedback app within Product Collection, helping customers find relevant evidence, discover meaningful themes, and receive useful suggestions and shape their roadmap in Jira Product Discovery.\nAs the first ML engineer on the team, you will establish the ML foundations of a new data product within the fast-growing Product Collection. You will have clear product direction and autonomy to experiment, make technical decisions, and ship improvements, working with product engineers, product management, and design.\nYou will build on Atlassian’s shared infrastructure and expertise, with the opportunity to help shape and grow the ML team as the product develops.\nOur customers bring varied datasets, terminology, and ways of working. The challenge is to deliver trustworthy results with minimal setup, while keeping response times and processing costs practical.\nAtlassians 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\nFinding the right customer evidence\nA useful answer starts with relevant evidence. You will help us improve how we retrieve feedback for a product question, balancing fast interactive search with deeper analysis in background workflows. This includes evaluating how we prepare and chunk data, choose embeddings, retrieve candidates and rerank results.\nDiscovering meaningful themes at scale\nCustomers need to understand recurring and emerging needs across many feedback items. You will help us combine clustering, retrieval and language-model synthesis to identify well-supported themes, preserve differences between customer segments and avoid duplicate or weak conclusions. You will also investigate how to refresh this analysis efficiently as new feedback arrives.\nMaking suggestions more useful over time\nAI can suggest tags, populate fields and connect feedback, insights and product ideas. You will help us use customer acceptance, rejection and edits to evaluate and improve these suggestions. The challenge is to learn from imperfect signals without reinforcing bias or repeatedly presenting suggestions customers have already dismissed.\nWhat you will do\nTurn product problems into testable ML hypotheses, then design experiments that compare approaches against clear baselines.\n\nBuild representative evaluation datasets and workflows, combining measurable checks with human judgement where quality is subjective.\n\nDevelop and improve retrieval, clustering and language-model pipelines, working with product engineers to integrate them into customer workflows.\n\nMake evidence-based trade-offs between quality, latency, cost and operational complexity. Use simpler approaches when they meet the need, and evaluate fine-tuning where it offers a measurable benefit.\n\nEvaluate performance across customer segments and dataset sizes, including sparse and imbalanced data, rather than relying only on aggregate metrics.\n\nHelp take capabilities from experimentation into production, with regression checks, monitoring, controlled rollouts and clear ways to diagnose failures.\n\nWork with product, design and engineering colleagues to interpret customer feedback and translate findings into priorities.\n\nReuse shared AI infrastructure and collaborate with platform teams, while developing evaluation methods and tooling that can support other Product Collection use cases.\n\nWhat success would look like\nYour initial focus would be agreed with the team. A proposed starting point is finding relevant customer evidence, because it supports the quality of downstream answers and insights.\nEstablish a shared baseline: We understand the most important failure patterns and can measure quality, response time and cost on representative examples.\n\nImprove a priority workflow: We have evidence that a selected approach improves the customer experience, with its limitations and operational trade-offs understood.\n\nMake improvement repeatable: The team can detect regressions, observe production behaviour and use the results to prioritise further work.\n\nThe role offers the opportunity to shape both customer-facing AI capabilities and the evaluation practices behind them, starting with Feedback and identifying useful opportunities for reuse across Product Collection.\nQualifications\nWe are looking for someone who combines applied ML judgement with strong software engineering. You should be comfortable working through ambiguous product problems and explaining what the evidence supports, what remains uncertain and what to try next.\nExperience delivering applied ML systems: You have taken an ML capability beyond a prototype and can explain how you evaluated it, integrated it into a product or service, and monitored or improved it after release.\n\nStrong programming and engineering fundamentals: Expertise in Python with and the ability to write performant production-quality code, familiarity with SQL, knowledge of Spark and cloud data environments (e.g. AWS, Databricks)\n\nRelevant technical depth: You have practical experience in one or more of information retrieval, semantic search, natural language processing, clustering or LLM-based applications, and can apply that experience to unfamiliar problems.\n\nEvaluation and experimentation skills: You can construct useful datasets, choose appropriate metrics, compare against baselines and investigate why offline improvements may not translate into better customer outcomes.\n\nSound data judgement: You understand issues such as data leakage, selection bias, imbalanced data, sparse labels and overfitting, and can explain how they affect design and evaluation choices.\n\nProduction awareness: You consider latency, inference cost, observability and changing data as part of the solution, and can work with engineers to diagnose and improve production behaviour.\n\nClear communication and collaboration: You can explain technical trade-offs to colleagues across disciplines and work with them to agree useful outcomes and practical next steps.\n\nExperience that would be useful\nYou do not need experience in every area below. These are opportunities to bring additional depth to the team.\nBuilding retrieval-augmented generation systems, including chunking, embeddings, hybrid search and reranking.\n\nClustering or discovering themes in large collections of unstructured text, with traceable supporting evidence.\n\nDesigning human evaluation, annotation or relevance-judgement processes for subjective outputs.\n\nUsing implicit or explicit user feedback to improve ranking, recommendations or other ML behaviour.\n\nWorking with cold-start scenarios, limited labelled data or heterogeneous customer datasets.\n\nRunning online experiments or controlled releases and investigating differences between offline and production results.\n\nModel fine-tuning, model selection or inference optimisation, with evidence of when the additional complexity is worthwhile.\n\nBuilding reusable evaluation tooling, regression checks or monitoring for multi-stage ML and LLM systems.\n\nBenefits & Perks\nAtlassian 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.\nAbout Atlassian\nAt 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 .\nIn 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":8766,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"Poland","iso":"PL","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["IT Service & Asset Management","Project & Work Management Software","Developer Tools","Intranet & Knowledge Management"],"lifecycle":[{"event":"open","at":"2026-11-12T13:11:32Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":11,"reasons":["conf:2","win:early","comp:brand"],"computed_at":"2026-11-12T15:11:33Z"},"pay":null,"html_url":"https://alion.io/job/atlassian-senior-machine-learning-engineer-15","json_url":"https://alion.io/job/atlassian-senior-machine-learning-engineer-15.json","meta":{"generated_at":"2026-11-12T15:11:33Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":31745,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-13T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.1,"usd":0.01,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":182},"rest":"https://alion.io/mcp/rest/get_company?id=182"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fatlassian-senior-machine-learning-engineer-15"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":2,"usd":0.2},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fatlassian-senior-machine-learning-engineer-15"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.1,"usd":0.01},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fatlassian-senior-machine-learning-engineer-15"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/atlassian-senior-machine-learning-engineer-15\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fatlassian-senior-machine-learning-engineer-15"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":4164598}},{"tool":"search_jobs","arguments":{"company":"atlassian.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":4164598},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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