Senior Machine Learning Engineer, Delivery Merchant

About the Company

Bolt is a rapidly expanding technology firm operating across Europe and Africa, serving over 200 million customers in more than 50 countries and 850 cities. The company is supported by a global workforce of over 4,000 employees and 4.5 million partners. Bolt is committed to fostering an inclusive workplace that welcomes everyone regardless of race, color, religion, gender identity, sexual orientation, national origin, age, or ability. The company's core mission is to create cities prioritizing people rather than cars.

Role Overview

The Senior Machine Learning Engineer will join Bolt's Delivery Merchant engineering group, collaborating closely with software engineers, product managers, and data scientists. The primary goal is to enhance and scale AI systems managing Bolt Food’s merchant catalog and opportunity sizing across more than 50 markets. Focus areas include improving ML model quality, ensuring robust production services, and expanding capabilities into areas such as agentic catalog workflows and merchant scoring. This position involves a significant strategic initiative to shift from reliance on third-party API models to proprietary fine-tuned open-weight models to improve economics, latency, and control.

Main Responsibilities

• Develop, train, and deploy machine learning and large language models (LLMs) that automate catalog enrichment, moderation, and classification at scale, while managing accuracy, latency, and operational costs.
• Lead the transition from third-party API models to self-hosted, fine-tuned open-weight models through data preparation, fine-tuning, model evaluation, and production deployment.
• Design and create AI-driven agentic systems for complex catalog automation workflows, including multi-step processes, tool integration, safeguards, and evaluation frameworks.
• Build and maintain comprehensive evaluation and experimentation infrastructure such as offline benchmarks, regression testing suites, LLM judgment pipelines, and A/B testing linked to business outcomes.
• Manage the deployment and operational costs of self-hosted models by optimizing quantization, throughput, GPU use, and carefully evaluating build versus buy decisions.
• Work cross-functionally with engineering, data science, product management, and ML platform teams to bring machine learning solutions into reliable production and monitoring.

Candidate Profile

• Proven track record designing, building, and deploying ML systems in production at scale, ideally within consumer-facing technology products.
• Experience delivering LLM-based solutions including prompt engineering, model iteration, evaluation, guardrails, observability, and controlling costs.
• Hands-on expertise in fine-tuning and operationalizing open-weight models (e.g., LoRA/QLoRA, SFT, preference optimization), including managing training datasets and production serving.
• Deep specialization in natural language processing or recommendation systems with demonstrable positive business impact.
• Strong software engineering background with proficiency in Python, SQL, and deep learning frameworks such as PyTorch, TensorFlow, JAX, or Triton, along with experience using modern ML tools and cloud infrastructure (AWS, SageMaker, Airflow, Docker).
• Excellent product intuition and proactive ownership, with the ability to convert ambiguous challenges into quantifiable ML-driven solutions and oversee their full lifecycle to production.

While experience is valued, Bolt welcomes applications from passionate, intelligent, and ethical individuals even if some qualifications are unmet.

Employee Benefits and Culture

• Opportunity to contribute directly to the evolution of urban mobility.
• Influence the experience of millions of users and partners spanning over 850 cities and 50 countries.
• Work within fast-paced, empowered teams composed of talented and supportive colleagues.
• Access unique career advancement prospects to rapidly develop professionally.
• Competitive salaries accompanied by stock options aimed at enabling focus on impactful work.
• Hybrid work setup requiring at least three days onsite weekly to enhance collaboration and team bonding.
• Programs supporting physical and mental wellness, tailored benefits may differ by location and role.