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Artificial Intelligence Researcher - microTECH Global LTD

Job in City Of London, Central London, Greater London, England, UK
Listing for: Jobster
Full Time position
Listed on 2026-01-16
Job specializations:
  • IT/Tech
    Data Scientist, Artificial Intelligence
Job Description & How to Apply Below
Location: City Of London

Artificial Intelligence Researcher - micro

TECH Global LTD

Job Location:

Cambridge or London, UK

Employment Type:

Full-time
Seniority Level: Mid-Senior level
Job Function:
Engineering and Information Technology

This is a permanent position requiring hybrid working in either Cambridge or London. Our client is looking for AI Researchers specializing in Reinforcement Learning with Human Feedback (RLHF) and Generative AI. In this role, you will design and optimise algorithms that align large-scale generative models with human preferences, ensuring they are safe, controllable, and capable of producing high-quality outputs across multiple modalities.

Responsibilities
  • Develop and refine RLHF algorithms for large language and generative models.
  • Research and implement deep reinforcement learning methods (policy gradients, actor‑critic, off‑policy learning) for model alignment.
  • Train, fine‑tune, and evaluate LLMs and diffusion models at scale.
  • Design experiments to align generative outputs with human and organisational preferences.
  • Collaborate with researchers, engineers, and human feedback teams to build scalable alignment pipelines.
  • Publish findings in top‑tier AI conferences and contribute to open‑source frameworks.
Key Requirements
  • PhD in Computer Science, Machine Learning, or related field.
  • Publications at NeurIPS, ICML, ICLR, ACL, or related venues.
  • Deep expertise in Reinforcement Learning (policy optimisation, reward modelling, RLHF).
  • Hands‑on experience training/fine‑tuning generative models (LLMs, diffusion, transformers, GANs).
  • Strong knowledge of deep learning frameworks (PyTorch, JAX, Tensor Flow).
  • Proficiency in Python and standard ML libraries.
  • Solid foundations in probability, optimisation, and statistics.
  • Experience working with large‑scale distributed training on GPUs/TPUs.

If this sounds of interest, please reach out to

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