{"id":1438729,"url":"https://alion.io/job/allegro-machine-learning-engineer-allegro-pay-2","title":"Machine Learning Engineer - Allegro Pay","company":{"id":1876195,"name":"Allegro","domain":"allegro.eu","url":"https://alion.io/company/allegro-eu","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"inferred_company_offices","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["PL"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":71000,"max_usd":199000,"period":"year","method":"global_role_seniority_cell","sample_n":399},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":".NET","optional":false},{"name":"Agentic Workflows","optional":false},{"name":"Azure","optional":false},{"name":"Claude","optional":false},{"name":"Dagster","optional":false},{"name":"FastAPI","optional":false},{"name":"Feature Store","optional":false},{"name":"Flask","optional":false},{"name":"Kedro","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false},{"name":"Amazon SageMaker","optional":true},{"name":"AWS","optional":true},{"name":"C#","optional":true},{"name":"Consul","optional":true},{"name":"dbt","optional":true},{"name":"Docker","optional":true},{"name":"GitHub","optional":true},{"name":"GitHub Actions","optional":true},{"name":"Service Mesh","optional":true},{"name":"Vertex AI","optional":true},{"name":"Windows","optional":true}],"status":"live","first_seen_at":"2026-09-03T02:00:00Z","employer_posted_date":"2026-09-03","last_verified_at":"2026-09-29T09:58:52Z","board_verified":false,"closed_at":null,"days_open":28,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":28},"description":"Job Description:\nAllegro Pay is Central Europe’s largest and fastest-growing FinTech - the only place where financial solutions can be created at such a large scale, using state-of-the-art technology. We work on purchase financing and payment methods used daily by customers of Allegro, the most popular shopping platform in Poland and one of the largest e-commerce companies in Europe. We seek talented people who want to create powerful and robust solutions supporting a product with over a dozen million active users.\nAs a Machine Learning Engineer, you will be responsible for the machine learning / data platform in Allegro Pay and support Data Scientists in building machine learning models, making business-critical decisions, and enabling shipping to production while ensuring their high availability and performance.\nIn your daily work you will handle the following tasks:\nDeveloping a modern MLOps ecosystem aimed at automating the process of building and deploying machine learning models.\nDesigning and implementing a modern platform for training predictive models (classifiers, deep neural networks, graph models).\nDeploying models to production and optimising deployment pipelines, to enable Data Scientists’ self-service.\nManaging, monitoring, and recalibrating models deployed to production.\nSupporting work on our Feature Store - an application providing predictors for models operating in production.\nFinding synergies and building connectors with FinTech AI platform, enabling agentic workflows to interact with our models and features.\nTechnologies you’ll encounter on the job: Python, Snowflake, Airflow, Azure, Kedro, MLFlow, .NET, Kubernetes, Tableau.\nWhy is it worth working with us, and what sets us apart:\nWe are an experienced team that is not afraid of hard problems and is constantly looking for development opportunities. We deploy our models at a scale found nowhere else in Poland.\nWe approach the development of analytical solutions with an engineering mindset, drawing from methodologies originating in classical software development.\nYou will be working on applications of ML in the finance sector, where the scale, sophistication of algorithms, impact on business, and technical requirements will be the key challenges.\nYou will directly influence predictive models which change the way how millions of Allegro customers interact with the platform, in real-time.\nOur employees regularly attend conferences in Poland and abroad (Europe & US), and each team has its own budget for training and study aids. If you want to keep growing and share your knowledge, we will always support you.\nThis is the right job for you if you:\nGraduated with a degree in Computer Science, Mathematics or another technical major.\nHave at least 2 years of experience in building ML-driven solutions.\nFluently program in Python, know libraries from the MLE’s toolchain (scikit-learn, PyTorch, Pandas, FastAPI/Flask) and use development tools with ease.\nAre comfortable and efficient with AI assisted programming tools (Opencode, Codex, Claude).\nHave some experience with modern Python-based orchestration tools (Airflow, Dagster).\nHave good analytical skills and know SQL.\nKnow and apply the DevOps principles in their work.\nUnderstand statistical and machine learning methods, especially algorithms based on decision trees and neural networks, on a practical level.\nAre able to make independent decisions within the scope of their responsibilities and take ownership of the code they created.\nNice-to-have:\nExperience with the .NET ecosystem.\nKnowledge of cloud-based MLOps tools (AzureML, Google Vertex AI, AWS Sagemaker).\nExperience with modern SQL-based data transformation frameworks - dbt.\nWhat's in it for you:\nFlexible working hours in the hybrid model (4/1) - working hours start between 7:00 a.m. and 10:00 a.m. We also have 30 days of occasional remote work.\nAnnual bonus based on your annual performance and company results (up to 10% of the gross annual salary depending on your end-year assessment).\nWell-located offices (with e.g. fully equipped kitchens, bicycle parking, terraces full of greenery) and excellent work tools (e.g., raised desks, ergonomic chairs, interactive conference rooms).\nA 16\" or 14\" MacBook Pro or corresponding Dell with Windows (if you don't like Macs) and all the necessary accessories.\nA wide selection of fringe benefits in a cafeteria plan - you choose what you like (e.g., medical, sports or lunch packages, insurance, purchase vouchers).\nEnglish classes that we pay for related to the specific nature of your job.\nA training budget, inter-team tourism, hackathons, and an internal learning platform where you will find multiple trainings.\nAn additional day off for volunteering, which you can use alone, with a team, or with a larger group of people connected by a common goal.\nSocial events for Allegro people - Spin Kilometers, Family Day, Fat Thursday, Advent of Code, and many other occasions we enjoy.\nAnd that's just the beginning! You can read more about the benefits here.\n#goodtobehere means that:\nYou will join a team you can count on - we work with top-class specialists who have knowledge- and experience-sharing in their DNA.\nYou will love our level of autonomy in team organization, the space for continuous development, and the opportunity to try new things. You get to choose which technology solves the problem and you are responsible for what you create.\nYou will value our Developer Experience and the full platform of tools and technologies that make creating software easier. We rely on an internal ecosystem based on self-service and widely used tools such as Kubernetes, Docker, Consul, GitHub, and GitHub Actions. Thanks to this, you can contribute to Allegro from your very first days on the job.\nYou will be equipped with modern AI tools to automate repetitive tasks, allowing you to focus on developing new services and refining existing ones (also leveraging AI support).\nYou will create solutions that will be used (and loved!) by your friends, family and millions of our customers.\nYou will meet the Allegro Scale, which starts with over 1000 microservices, an open-source data bus (Hermes) with 300K+ rps, a Service Mesh with 1M+ rps, tens of petabytes of data, and production-used machine learning.\nYou will become part of Allegro Tech - We speak at industry conferences, cooperate with tech communities, run our own blog (it's been over 10 years!), record podcasts, lead guilds, and we organize our own internal conference - the Allegro Tech Meeting. We create solutions we love (and can) to talk about!\nSend us your CV and... see you at Allegro!","description_format":"text","description_chars":6640,"description_truncated":false,"requirements":{"experience_years_min":2,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Apple Macbook","Cafeteria","Flexible schedule"],"hiring_locations":[{"name":"Poland","iso":"PL","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Commerce","Marketplaces"],"lifecycle":[{"event":"open","at":"2026-09-29T04:13:31Z"}],"liveness":{"score":30,"band":"fade","label":"Fading","p_open":1,"p_active":0.692,"p_room":0.44,"age_days":28,"expected_fill_days":22,"reasons":["conf:43","velocity","win:tail","crowd:junior,brand"],"computed_at":"2026-10-01T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/allegro-machine-learning-engineer-allegro-pay-2","json_url":"https://alion.io/job/allegro-machine-learning-engineer-allegro-pay-2.json","meta":{"generated_at":"2026-10-01T18:30:25Z","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":845,"day_limit":5000,"remaining_today":4155,"minute_limit":60,"resets_at":"2026-10-02T00:00:00Z"}}}