Senior Machine Learning Engineer

Role overview

Key responsibilities

• Deploy and fine-tune LLMs for domain-specific tasks; build evaluation infrastructure and benchmarking pipelines.
• Apply reinforcement learning, knowledge representation, and automated decision-making to complex problems.
• Build data pipelines for ingestion, transformation, versioning, and labelling, and maintain reproducible development loops.
• Orchestrate multi-model agentic workflows using function calling, tool use, RAG, and code interpreters.
• Integrate AI solutions into production products in partnership with production teams and software engineers.
• Provide technical leadership and mentor junior engineers.

Required qualifications

• Master’s or PhD in computer science, mathematics, physics, electrical engineering, or a related technical discipline.
• Strong programming skills in one or more of C, C++, R, Java, or Python.
• Advanced knowledge of LLM/VLM architecture, post-training methods, and evaluation pipelines.
• Experience in data and system engineering for large-scale model workflows and deep-learning libraries such as TensorFlow or PyTorch.
• Fluency with one listed AI coding tool or a similar tool.
• Enterprise machine-learning and AI project experience; ability to manage deadlines, workloads, and stakeholder communication.
• Adaptability, enthusiasm for learning, and problem-solving aptitude.
• Industrial real-world LLM application, fine-tuning, optimization, and deployment experience is a plus.

Work arrangement

• Hybrid working is offered, with two days at home and flexible working.

Benefits

• Benefits include a bonus scheme, annual leave with flexible holiday buying and selling, a pension, wellbeing support, and flexible benefits.
• Additional benefits include private medical and dental programmes, a cycle purchase scheme, and a relaxed dress code.