{"id":141559,"url":"https://alion.io/job/ocado-group-senior-machine-learning-engineer-e3","title":"Senior Machine Learning Engineer (E3)","company":{"id":51471,"name":"Ocado Group","domain":"ocadogroup.com","url":"https://alion.io/company/ocado-group","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":83,"open_postings":18,"ghost_share":0,"stale_share":0.667,"repost_share":0,"time_to_fill_p50_days":45,"computed_at":"2026-10-16T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Kraków, Poland","Poland"],"countries":["PL"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":23600,"max":29900,"currency":"PLN","period":"month","gross":null,"usd_annual":91800},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AWS","optional":false},{"name":"BigQuery","optional":false},{"name":"Feature Store","optional":false},{"name":"GCP","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Java","optional":false},{"name":"LLM","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Recommender Systems","optional":false},{"name":"SQL","optional":false},{"name":"Vertex AI","optional":false}],"status":"live","first_seen_at":"2026-07-29T14:56:52Z","employer_posted_date":"2026-09-23","last_verified_at":"2026-10-16T16:54:15Z","board_verified":true,"closed_at":null,"days_open":79,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":79},"description":"About Us\nWe are Ocado Group, and we're bringing world-class automation to online grocery. Our Ocado Smart Platform (OSP) combines cutting-edge robotics, AI, and IoT within our advanced CFCs (Customer Fulfilment Centres). We've mastered the single pick, transforming online delivery for our global partners. Join us and be part of a team pushing the boundaries of retail technology.\nAbout the team \nSmarties is the Data Science and intelligent personalisation team within the Ecommerce stream at Ocado Technology - the part of Ocado Smart Platform (OSP) that powers the end-customer shopping experience. We build the prediction, ranking, and recommendation capabilities that power smart features across OSP and that product teams integrate into customer-facing journeys.\nWe are a cross-craft team combining data scientists, software engineers, and ML engineers - a mix of skills that lets us work across the full stack of a smart feature, from data and modelling to production systems.\nOur mission is to make smart feature development scalable and self-serve. We turn the hard parts of \"smarts\" - prediction, ranking, personalisation - into reusable building blocks, proven on our own products first, so that over time any product team can build intelligent features using our platform and our support.\nWe own and operate a broad portfolio of Data Science systems in production: checkout walk personalisation, order prediction and recommendations, customer intelligence, search and browse ranking, recipe discovery, and the web analytics data pipeline that underpins measurement across Ecommerce. We are in an active phase of evolution - consolidating shared capabilities, reducing complexity, and opening space for new initiatives - while progressively shifting toward a platform model where product teams build on shared Data Science capabilities.\nUnderpinning all of this is data quality. Smart features are only as good as the data behind them. We treat reliable, well-understood data as a foundation, not a side task - and we actively contribute to raising that standard across Ecommerce.\nEcommerce is also a genuinely good place to be a technical engineer right now. Practically all the way up the reporting line you will find people with strong technical backgrounds who share a vision for what modern software development looks like - including how AI tools and coding assistants are changing what high-quality engineering means in practice. That perspective shapes how we work, what we invest in, and how we develop people.\nJob Purpose\nA Senior ML Engineer in Smarties brings the engineering perspective to a cross-craft team: raising the bar on how ML systems are built, not just how they perform. This means championing industry best practices across the full ML lifecycle - from experimentation and model design through to serving, monitoring, and cost management - and translating that expertise into systems that are reliable, testable, scalable, and maintainable in production.\nThe role carries real ownership: of the systems, of the decisions behind them, and of their long-term health. We expect someone who tracks how the field is moving, knows when to adopt and when to hold, and actively shapes how the team builds - not just what it builds.\nKey responsibilities: \nInfluence - Be an individual with positive impact \nActively teach, coach, and create opportunities for others to share and develop expertise - both within the team and across the wider organisation.\nAct as a technical leader and consultant on ML within the team's domain, raising the quality bar on how ML problems are defined and solved.\nWork across craft boundaries: collaborate effectively with data scientists and software engineers, helping the team function as one cross-craft unit rather than parallel silos.\nPromote realistic commitments at the team level; openly share trade-offs, uncertainties, and technical risks.\nRepresent ML thinking in conversations with product managers, data analysts, and consuming engineering teams.\nExecution and Delivery - Get value out of the door \nTake ownership of the full ML lifecycle - from problem framing and data exploration through training, evaluation, deployment, and monitoring (MLOps) - on production systems that directly affect revenue and customer experience.\nMake key design decisions on ML/AI components within our portfolio, weighing technical, cost, and product implications explicitly.\nBreak work into incremental, reversible steps that deliver value early and reduce risk.\nHandle production incidents and degradations effectively, diagnosing root causes and resolving them at the right speed.\nDeliver with a high degree of autonomy; contribute to technical direction within the team.\nContribute to both the ongoing health of production systems and the emergence of reusable platform building blocks - extracting from real problems, not building speculatively.\nInsights - Solve real problems \nAnalyse production ML systems, identify failure modes, and drive improvements grounded in data.\nDesign and implement well-defined experiments to validate hypotheses, from offline evaluation to live AB tests.\nSeek the root of the problem being solved; translate business requirements into correctly scoped ML problems.\nProactively surface data-driven insights to technical and non-technical audiences.\nContribute to raising data quality standards across the team's pipelines - treating reliable, well-understood data as a foundation for all ML work.\nStrategy - Keep an eye toward the future\nApply sufficient domain knowledge to make good design decisions across the team's ML portfolio.\nIdentify opportunities to simplify the live system - reducing support burden, compressing costs, and building leverage (for example, replacing several bespoke models with a single configurable engine).\nApply appropriate testing and de-risking principles, especially when working on systems with direct business impact.\nInvestigate and resolve production issues, raising awareness where these stretch beyond the team.\nCollaborate with the Engineering Manager and Data Science leads on the technical direction of the platform.\nUnderstand consuming teams' needs well enough to design building blocks that serve them without creating new dependencies.\nKnowledge, skills and experience: \nRequired: \nML engineering in production: demonstrated experience owning the full Data Science lifecycle - data, training, evaluation, deployment, serving, and ongoing operations - on production systems, not just prototypes. This includes monitoring for data drift, training/serving skew, and model degradation; the production discipline, not just the modelling.\n Data fluency: strong working knowledge of data processing tools - BigQuery (SQL), pandas, Dataflow, or equivalents - and the ability to reason about data quality as a first-class concern.\nML fundamentals: fluency across supervised and unsupervised learning, ranking models, recommendation systems, evaluation metrics, overfitting, and model selection.\n Cloud platform experience: practical experience with GCP (our primary environment - Vertex AI, BigQuery, Dataflow) and AWS, both of which are in use across our platform.\nPython: strong production-grade Python; comfortable in a codebase where your models and services are consumed by other engineers.\nTechnical leadership: experience leading the technical design of ML projects, including articulating trade-offs and driving decisions within a team.\nValued: \nExperience with LLM-based systems (RAG, structured generation, evaluation, cost management), particularly on GCP/Vertex AI or similar.\nExperience building or contributing to shared ML infrastructure or internal platforms (feature stores, serving layers, shared pipelines).\nFamiliarity with recommendation systems, personalisation, or search ranking at scale.\nExperience working in a cross-craft team alongside software engineers and data scientists.\nUnderstanding of AB testing methodology and experimentation platforms.\nFamiliarity with Java - our model serving layer is built on Java services.\nWhat we are looking for beyond the CV\nSmarties is a small team carrying a large portfolio. The ideal candidate is someone who:\nTakes ownership seriously - not just of the model, but of the system, the data, and the outcome.\nThinks in leverage - prefers a solution that improves ten products at once over ten separate solutions.\nIs honest about uncertainty - raises problems early, shares trade-offs openly, and does not treat \"keeping the lights on\" as someone else's job.\nWorks well across disciplines - can design with a data scientist, review a service with a software engineer, and explain an ML decision to a product manager.\nCares about data quality - understands that smart features are only as good as the data behind them, and acts accordingly.\nThis role description summarises the typical functions of the role. It is not an exhaustive list of all possible responsibilities, tasks, or duties.\nBENEFITS:WHAT’S IN IT FOR YOU?\nWork and life should fit together, so we offer a range of benefits focusing on well-being, development, and team spirit. The final package will depend on the contract type we agree on.\nStandard benefits: life insurance, private health care [Luxmed], Multisport card, lunch vouchers, company share programs, and assistance for everyday worries and serious health problems.\nLearning opportunities: access to the Learnebly platform and LinkedIn Learning, English classes, and a book library, \nParental support: additional +10 days maternity / +20 days paternity leave, funding for nurseries and kindergartens\nHybrid model: 2 days/week in the office and flexibility to work from almost any location for up to 30 days a year.\nOffice perks: centrally located offices with car and cycling parking, and home office equipment provided.\nCareer growth: a clear career path with opportunities to rotate between projects, teams, domains and roles under the guidance of highly skilled senior colleagues \nHigh engineering culture: unique software engineering culture with a high level of test coverage and agile environment [read about our tech stack and engineeringpractices across Ocado Technology]\n\nSpeaker community: public speaking training and support for preparing presentations at conferences or meetups, including our own Ocado Technology Meetups\nPassions groups: running, cycling and more \nAnnual celebrations: carnival, summer parties, family picnics, or kid’s days.\n\nAt Ocado Technology, we're always exploring, learning, and implementing new initiatives, and we're eager to share stories, insights, and experiences with you. Meet our team members during meetups [ Watch recordings here]\n#LI-KP1 #LI-HYBRID\nGross Monthly Pay range\nPoland Monthly Pay Range\n23 600 zł—29 900 zł PLN","description_format":"text","description_chars":10786,"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":["Home office","Life insurance"],"hiring_locations":[{"name":"Poland","iso":"PL","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Logistics & Warehouse Robotics","Online Groceries","Supply Chain & Logistics Software"],"lifecycle":[{"event":"open","at":"2026-07-29T14:56:52Z"}],"visa":[],"liveness":{"score":16,"band":"cold","label":"Long shot","p_open":1,"p_active":0.445,"p_room":0.36,"age_days":78,"expected_fill_days":45,"reasons":["conf:2","stale_co","win:tail","crowd:"],"computed_at":"2026-10-16T05:45:00Z"},"pay":{"stated_usd_annual":91800,"is_top_pay":false},"html_url":"https://alion.io/job/ocado-group-senior-machine-learning-engineer-e3","json_url":"https://alion.io/job/ocado-group-senior-machine-learning-engineer-e3.json","meta":{"generated_at":"2026-10-16T18:24:11Z","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":3446,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-17T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"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":51471},"rest":"https://alion.io/mcp/rest/get_company?id=51471"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Focado-group-senior-machine-learning-engineer-e3"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"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%2Focado-group-senior-machine-learning-engineer-e3"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"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%2Focado-group-senior-machine-learning-engineer-e3"},{"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/ocado-group-senior-machine-learning-engineer-e3\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Focado-group-senior-machine-learning-engineer-e3"}]}