Research Scientist -Machine Learning

Huawei Technologies Research & Development (UK) Ltd
City of London
2 weeks ago
Applications closed

Related Jobs

View all jobs

Research Scientist (Quantum Chemistry and Machine Learning), London

Research Scientist, Machine Learning (PhD)

Research Scientist / Engineer in NLP (Contractor)

Research Scientist -Machine Learning

Research Scientist (Quantum Chemistry and Machine Learning), London London

Research Scientist (Machine Learning)

Job Summary

The Reinforcement Learning Team at the Huawei London Research Centre is seeking a highly skilled and research-driven Machine Learning Scientist to join our team. This role focuses on advancing the state-of-the-art in reinforcement learning, Bayesian optimisation, AI agents, large language models (LLMs), and/or vision-language models (VLMs). You will work at the intersection of fundamental research and applied innovation, developing novel algorithms, architectures, and systems that push the boundaries of AI capabilities.

This is a unique opportunity to contribute to high-impact AI research while collaborating with a multidisciplinary and multinational team of scientists and engineers. We value scientific excellence, demonstrated by a strong publication record at top-tier venues, and an eagerness to translate cutting-edge ideas into working prototypes and real-world applications.


Key Responsibilities

  • Conduct original research in RL, BO, AI agents, LLMs, and VLMs, leading to publications in top conferences and journals (e.g., NeurIPS, ICLR, ICML, JMLR, and others).
  • Design and implement new algorithms and models that enable advanced reasoning, planning, perception, and multimodal understanding.
  • Design and implement new algorithms for efficient decision-making under uncertainty with applications to chemistry, physics, open math problems, and robotics.
  • Collaborate with cross-functional teams to integrate research outputs into scalable systems and real-world use cases.
  • Explore novel ways to align and enhance AI agents for complex, open-ended tasks.
  • Actively engage with the broader research community through publications, talks, and open-source contributions.
  • Mentor junior researchers and contribute to the scientific culture of the team.

Person Specification

  • Required:

    • PhD (or equivalent research experience) in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
    • Strong research track record with publications at top-tier ML/AI venues: ICML, ICLR, JMLR, NeurIPS and the like.
    • Deep expertise in at least two of the following: reinforcement learning, Bayesian optimisation, AI agents, LLMs, VLMs.
    • Proficiency in Python and experience with at least one major ML framework (PyTorch, TensorFlow, or JAX).
    • Ability to work in a fast-paced, research-oriented environment with ambiguous and evolving goals.
    • Excellent problem-solving, collaboration, and communication skills.
    • Ability to lead a team of junior researchers and engineers.
    • Passion for bridging fundamental AI research with impactful applications.


What We Offer

  • 33 days annual leave entitlement per year (including UK public holidays)
  • Group Personal Pension
  • Life insurance
  • Private medical insurance
  • Medical expense claim scheme
  • Employee Assistance Program
  • Cycle to work scheme
  • Company sports club and social events
  • Additional time off for learning and development


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.