Senior Machine Learning Engineer, Delivery Merchant

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.

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.

• 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.

• Proven experience building and shipping ML systems in production at scale, ideally in consumer-facing product environments at a technology company.
• Demonstrated track record taking LLM-based systems to production, including prompt/model iteration, evaluation, guardrails, observability, and cost management.
• Hands-on experience fine-tuning and serving open-weight models (e.g. LoRA/QLoRA, SFT, preference optimisation), including building training data and managing production serving.
• Deep expertise in NLP or recommendation systems, with models that have moved a business metric.
• Strong engineering fundamentals: mastery of Python and SQL, clean code practices, production experience with a deep learning framework (PyTorch, TensorFlow, JAX, or Triton), and experience with modern ML tooling and cloud infrastructure (AWS, SageMaker, Airflow, Docker).
• Strong product sense and proactive ownership, with the ability to turn ambiguous problems into measurable ML solutions and drive them from discovery to production impact.

• Play a direct role in shaping the future of mobility.
• Make an impact on millions of customers and partners across 850+ cities in 50+ countries.
• Work in fast-moving, autonomous teams with talented and supportive colleagues.
• Accelerate your professional growth with unique career opportunities.
• Receive a rewarding salary and stock option package that lets you focus on doing your best work.
• Enjoy the flexibility of hybrid working, with a minimum of 3 days in the office each week to foster strong connections and teamwork.
• Take care of your physical and mental health with our wellness perks.