machine learning engineer

Bolt is a technology company operating mobility and delivery services across 50+ countries and 850+ cities, serving over 200 million customers through a platform supported by 4.5+ million partners and 4,000+ employees. Its mission is to make cities for people, not cars.

• Design, train, and deploy ML/LLM models for catalogue enrichment, moderation, and categorisation at scale, owning production accuracy, latency, and cost;
• Drive the transition from frontier API models to fine-tuned open-weight models by building data curation and fine-tuning pipelines, comparing quality, and taking selected models into production;
• Design and build agentic AI systems for catalogue automation, including multi-step workflows, tool use, guardrails, and evaluation harnesses;
• Build evaluation and experimentation infrastructure, including offline benchmarks, regression suites, LLM-as-judge pipelines, and A/B tests linked to business metrics;
• Own serving and cost management for self-hosted models, including quantisation, throughput tuning, GPU utilisation, and build-versus-buy decisions;
• Collaborate with Software Engineers, Data Scientists, Product Managers, and the ML Platform team to productionise and monitor ML solutions.

• Proven experience building and shipping production ML systems at scale, ideally for consumer-facing products at a technology company;
• Experience taking LLM-based systems to production, including prompt and model iteration, evaluation, guardrails, observability, and cost management;
• Hands-on experience fine-tuning and serving open-weight models, including LoRA/QLoRA, SFT, preference optimisation, training data development, and production serving;
• Deep expertise in NLP or recommendation systems, with models that improved a business metric;
• Mastery of Python and SQL, clean code practices, and production experience with a deep learning framework such as PyTorch, TensorFlow, JAX, or Triton;
• Experience with modern ML tooling and cloud infrastructure, including AWS, SageMaker, Airflow, and 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;
• Nice to have: experience in consumer-facing product environments.

• Rewarding salary and stock option package;
• Hybrid working with a minimum of 3 days in the office each week;
• Wellness perks supporting physical and mental health;
• Career opportunities and professional growth;
• Work on products serving millions of customers and partners across 850+ cities in 50+ countries;
• Some perks may differ depending on location and role.