Artificial Intelligence Researcher

microTECH Global LTD; Greater London; Hybrid; GBP 60,000 - 80,000; Full time

Job summary

A tech company specialized in AI research seeks skilled AI Researchers in Reinforcement Learning with Human Feedback. As part of a dynamic team, you will develop and optimize algorithms that align generative models with human preferences. The ideal candidate holds a PhD and has deep expertise in RL, strong knowledge of deep learning frameworks, and experience in working with large-scale training. This permanent role offers a hybrid working model in Cambridge or London, along with opportunities to contribute to cutting-edge research.

Qualifications

• Publications at NeurIPS, ICML, ICLR, ACL, or related venues.
• Deep expertise in Reinforcement Learning (policy optimisation, reward modelling, RLHF).
• Experience working with large-scale distributed training on GPUs/TPUs.

Responsibilities

• Develop and refine RLHF algorithms for large language and generative models.
• Research and implement deep reinforcement learning methods for model alignment.
• Train, fine-tune, and evaluate LLMs and diffusion models at scale.
• Design experiments to align generative outputs with human preferences.
• Collaborate to build scalable alignment pipelines.
• Publish findings in top-tier AI conferences.

Skills

Reinforcement Learning

Generative AI

Deep learning frameworks

Python

Probability

Optimisation

Statistics

Education

PhD in Computer Science, Machine Learning, or related field

Tools

PyTorch

JAX

TensorFlow

Job description

This is a permanent position with candidates required to do hybrid working in either Cambridge or London.

Our client are looking for AI Researchers specialising in Reinforcement Learning with Human Feedback (RLHF) and Generative AI. In this role, you will design and optimise the 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. You’ll sit at the intersection of RL, LLMs, and generative modelling, helping us build the next generation of foundation models

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).
• Strong knowledge of deep learning frameworks (PyTorch, JAX, TensorFlow).
• 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 [e-mail]