About the job

We're looking for a Senior Machine Learning Engineer to own and scale the AI systems behind Bolt Food's merchant catalogue and opportunity sizing systems, spanning LLM-based enrichment and categorisation through to fine-tuned open-weight models we train and serve ourselves, all in service of automating merchant operations across 50+ markets.

About Us

With over 200 million customers across 50+ countries and 850+ cities, Bolt is one of the fastest-growing tech companies in Europe and Africa — powered by 4.5+ million partners on our platform, 4,000+ employees globally. And it's all thanks to our people.

We believe in creating an inclusive environment where everyone is welcome, regardless of race, colour, religion, gender identity, sexual orientation, national origin, age, or ability.

Our ultimate goal is to make cities for people, not cars — and we need your help on this mission!

About The Role

You'll join the Delivery Merchant engineering group alongside software engineers, product managers, and data scientists who build the systems our merchant partners depend on. Your mission is to make our ML-powered catalogue automation substantially better by raising model quality, hardening services into reliable production systems, and extending into new areas like agentic catalogue workflows and merchant scoring.

There's also a significant frontier here. Today we rely heavily on third-party API models. You'll help drive our transition to fine-tuned open-weight models we own and serve ourselves, giving us better economics, lower latency, and more control. This is early-stage, high-leverage work where you'll shape the technical direction.

This is a hybrid, end-to-end role. You'll work from ambiguous business problems through offline evaluation, online experiments, and production systems, and you'll stay accountable for the outcomes.

Main Tasks And Responsibilities

• Design, train, and deploy ML/LLM models that automate catalogue enrichment, moderation, and categorisation at scale, owning accuracy, latency, and cost in production.
• Drive the transition from frontier API models to fine-tuned open-weight models: build data curation and fine-tuning pipelines, run quality comparisons, and take winners into production.
• Design and build agentic AI systems for catalogue automation, covering multi-step workflows, tool use, guardrails, and the evaluation harnesses to prove they work.
• Build evaluation and experimentation infrastructure: offline benchmarks, regression suites, LLM-as-judge pipelines, and A/B tests tied to business metrics.
• Own the serving and cost story for self-hosted models, including quantisation, throughput tuning, GPU utilisation, and build-versus-buy decisions.
• Collaborate cross-functionally with Software Engineers, Data Scientists, Product Managers, and the ML Platform team to productionise and monitor ML solutions.