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.

Information about the position

As Lead Site Reliability Engineer, you’ll own the reliability strategy and architectural evolution of Vinted’s ML Deployment platform. Your mission is to ensure that every machine learning model—from classic algorithms to Large Language Models (LLMs)—is served with industry-leading stability and performance.

You’ll join the ML Platform team, which owns the tooling for ML/LLM development, deployment and other platform capabilities that increase ML/AI delivery speed at Vinted. You won’t just be managing services; you’ll be shaping the architectural strategy for how Vinted serves AI at scale, and you'll act as a multiplier for the engineering function by mentoring senior engineers and driving domain-wide standards.

In this role, you’ll partner closely with Data Scientists and Production Engineering teams to translate Vinted’s ML innovation into production reality. You will be responsible for orchestrating one of the region’s largest GPU fleets—over 200 high-performance GPUs dedicated to production inference and LLM serving, including next-generation hardware designed for the most demanding AI workloads.

Our tech stack: Kubernetes, Terraform, Chef, Google Cloud Platform, Go, Kafka, Vespa, Redis, Vitess.

In this position, you’ll

Lead the technical roadmap for ML Deployment, ensuring our inference infrastructure scales to meet Vinted’s growing ML/AI demands.
Architect high-performance inference solutions, focusing on low-latency delivery and efficient GPU utilisation across our clusters.
Define and drive reliability standards, including SLOs, error budgets, and sophisticated observability for model-serving systems.
Mentor and grow senior engineers within the team, fostering a culture of technical excellence, pragmatism, and continuous learning.
Identify and solve systemic bottlenecks in our deployment lifecycle by writing RFCs and sparring with colleagues on long-term architectural improvements.
Oversee the integration of deployment workflows with automated experimentation patterns, such as A/B testing and shadow deployments.
Communicate with stakeholders about architectural risks, planned changes, and strategic reliability trade-offs.

About you

Have a track record of leading technical problem-solving for complex, domain-level systems.
Deeply experienced with Kubernetes, Linux internals, and high-scale networking.
Strong programming background in Go or Python.
Choose the right tool for the job based on long-term impact rather than personal preference.
Excellent written and spoken English, with the ability to articulate technical risks to both technical and non-technical stakeholders.
Natural coach and mentor who finds more value in enabling an entire team than in writing every line of code yourself.
Advantage: Experience with GPU workloads and inference frameworks like KServe, vLLM, or NVIDIA Triton.
Advantage: Knowledge of LLM evaluation and observability tools (e.g. Langfuse).

Work perks

The opportunity to benefit from our share options programe
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
Mental and emotional health support through the Mindletic app
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.

Equal Opportunity

The Vinted Group is committed to building an inclusive workplace where people from all walks of life feel a sense of belonging. We welcome applications from people of all backgrounds, identities and life experiences. At Vinted, all applicants are treated fairly without regard to their race, age, religion or belief, sex, national origin, citizenship, gender identity, sexual orientation, disability, or any other protected characteristic.

The gross monthly salary range for this position is:

€6.767—€9.150 EUR

Lead Site Reliability Engineer, ML Platform