Research Engineer: Generative AI & ML Systems (Hybrid)

Principle HR; Greater London; Hybrid; GBP 64,000 - 71,000; Full time

Job summary

Principle HR in London is seeking a Research Engineer to advance AI research and ML systems. The role blends hands-on engineering with rigorous experimentation on large models, reinforcement learning, synthetic data, and multi-agent setups.

You will work within a world-leading technology organization, contributing to cutting-edge infrastructure and research initiatives in a fast-moving, ambiguous environment where priorities evolve rapidly.

Qualifications

• 2+ years' hands-on Machine Learning/AI experience.
• Strong Python programming skills.
• Experience developing ML models at scale.
• Experience running complex experiments with large AI models and datasets.
• Strong understanding of the scientific/research process and the ability to investigate why approaches succeed or fail.
• Comfortable operating in a fast-moving, highly ambiguous research environment where priorities can change quickly.
• Experience with PyTorch, LLMs, LLM post-training, reinforcement learning, generative AI or multi-agent systems would be particularly valuable.
• A research-focused Master's or PhD in ML/AI is advantageous but not essential.

Responsibilities

• Combine hands-on engineering with research on ML systems.
• Develop robust research infrastructure and run complex experiments.
• Collaborate with researchers on cutting-edge AI projects.

Skills

ML/AI Experience

Python

Experimentation

Research Process

Adaptability

Generative AI

Tools

PyTorch

LLMs

Reinforcement Learning

Multi-agent Systems

Job description

Principle HR in London is seeking a Research Engineer to advance AI research and ML systems. The role blends hands-on engineering with rigorous experimentation on large models, reinforcement learning, synthetic data, and multi-agent setups.

You will work within a world-leading technology organization, contributing to cutting-edge infrastructure and research initiatives in a fast-moving, ambiguous environment where priorities evolve rapidly.