Brief info about Vinted
Our mission is to make second-hand the first choice, and we're looking for people who want to help us get there. Every day, we work together to help our members buy and sell pre-loved clothing and lifestyle items, giving each piece a second life – or even a third.
The Vinted Group is made up of three business units that support this mission:
Vinted Marketplace
is Europe’s leading platform for second-hand fashion and a go-to destination for all kinds of pre-loved items, with a growing range of categories. Our platform connects millions of members across 20+ markets, helping great items find a new life.
Vinted Go
enhances the shipping experience with a vast network of over 500,000 pick-up and drop-off points, partnering with more than 60 carriers across Europe, with added services like item verification for peace of mind on high-value pieces.
Vinted Pay
is the newest part of the Vinted Group, dedicated to bringing secure, reliable payments to buyers and sellers across Europe. Seamlessly integrated into the Vinted app, it helps keep every transaction safe, efficient, and easy for our members.
Founded in 2008 in Lithuania, Vinted began as a way for friends to find new homes for clothes they no longer needed. In 2019, we became Lithuania's first unicorn! Today, our headquarters remain in Vilnius, and we've grown with offices across Europe, supported by a team of over 2,000 people. Our backers include Accel, EQT Growth, Insight Partners, Lightspeed Venture Partners, Sprints, and TPG.
Information about the position
The Buyer domain's Data Science teams build and iterate on our most critical ML systems—like ranking, retrieval, and recommendations—that serve millions of users. As our ambition and the complexity of these models grow, we are scaling our MLOps capabilities to match: We are hiring an MLOps Engineer to solve this. This is a high-impact "bridge" role, not to build new platforms, but to be the expert user of our existing platforms (GCP/Vertex AI, Vespa, Triton). Your mission is to take our Data Science team's work, rectify the current ad-hoc solutions, and build the robust, automated, and high-quality "path-to-production" our advanced models require. Also by joining Vinted, you’ll become part of an active community of engineers who are keen on sharing knowledge, organizing learning workshops and so much more!
In this position, you’ll
- Productionize ML models: be responsible for migrating all existing model training code from experimental or proof-of-concept stages to robust and automated pipelines.
- Build continuous training (CT) pipelines: design, build, and maintain automated workflows for data preparation, model training, and batch scoring.
- Automate model serving & integration: package and deploy traditional ML models (e.g., in ONNX format) to our existing serving infrastructure, primarily Vespa and/or Triton.
- Close the deployment loop: automate model deployments where they are still manual. For example, building a GitHub Action that deploys a new model to Vespa, triggered by a webhook from a successful Vertex AI training pipeline.
- Build the missing quality gates: build the automated quality and evaluation gates that our pipelines lack today. This includes: Data quality gates: automatically check for upstream data issues in BigQuery before training. Model quality gates: automatically evaluate model performance against a baseline to prevent "bad" models from being promoted to production.
- Establish and uphold ML Model Lifecycle Standards: Take ownership of and continuously improve the standards, quality, and governance of the complete ML model lifecycle.
How You'll Work
- You are embedded in the Data Science Team: you will sit with the Data Scientists, participate in their planning, and be proactive in collaboratively resolving MLOps challenges.
- You are the "ML Software Engineer": you are the technical "bridge" and "enabler" on the team, owning the software engineering and automation components of the ML lifecycle.
- You are the "Expert Customer": you will be the expert user of the central ML Platform and Search Platform tools. You will work with them to define and improve platform-level features and "packaging contracts."
- Your Customer is the Data Scientist: your primary internal customer is the Data Scientist on your team. They will hand off a finalized model or training code, and you will own its path to production.
Work perks
- The opportunity to benefit from our share options programme
- 25 working days of holiday
- Access to all the tools & tech needed for work
- Home office support: we provide IT workstation equipment and a personal budget of up to €540 for home workplace furniture
- Private health insurance
- Digital mental and emotional health support and Employee Assistant Program (EAP)
- Frequent team-building events
- A personal monthly budget for shopping on Vinted
- The opportunity to spend up to 90 days per year - 21 of which can be spent working outside of the EU - on workation
- A dog-friendly office
- In Vilnius office: Gym & in-house meals at friendly prices
- In Kaunas office: a monthly lunch allowance, and a once-a-week provided in-house lunch and breakfast
Working at Vinted
Individual Learning Budget
We invest in your professional growth! As part of our commitment to continuous learning, we offer an annual learning budget to support your personal and career development through courses, certifications, workshops and more.
Hybrid Work
We’ve adopted a hybrid workplace model where 2 days in the office are recommended but not enforced. It’s up to you and your team to decide on the exact days you’ll spend working together in person.
About you
We are looking for an "ML-literate" Software Engineer, not a Data Scientist. You are a builder and an automator with a strong service mindset. * Must-have technical skills: deep expertise in GCP/Vertex AI (or equivalents like SageMaker), Kubernetes, and Docker. * Automation expertise: proven experience building robust CI/CD and automation pipelines (e.g., GitHub Actions, Jenkins, etc.). * ML-Literate: you understand ML workflows and concepts (training, evaluation, inference) well enough to debug pipelines and build meaningful quality gates. * Soft skills: a strong "service mindset," excellent communication, and the ability to act as a bridge between the highly technical worlds of Data Science and Infrastructure. You are pragmatic and driven to get high-quality work into production. * Bonus Points: familiarity with ML serving platforms like Vespa search engine or NVIDIA Triton and model formats like ONNX. * Willingness to learn and master new technologies and concepts. * We value pragmatism, big-picture thinking, curiosity, and problem-solving skills.
