Machine Learning Researcher - LLM/VLM

Staines
10 months ago
Applications closed

Related Jobs

View all jobs

Genomic Data Scientist in Rare Disease (we have office locations in Cambridge, Leeds & London)

Machine Learning Engineer (Forward Deployed)

Machine Learning Engineer - £110k – £130k – Geospatial Tech 4 Good

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior RF Data Scientist / Research Engineer

Machine Learning Researcher - LLM/VLM

Are you a PhD-educated Machine Learning Researcher looking for a new opportunity? If so, our client, a global consumer electronics company, is actively expanding their team. This role is based at one of their flagship AI centres in Cambridge, Cambridgeshire.

Key Responsibilities:

As a Machine Learning Researcher, you will:

Work on on-device LLMs and VLMs, as well as adaptive inference methods and mobile ML systems.
Conduct cutting-edge research and translate findings into practical applications, contributing to the commercialisation of AI across millions of devices.
Design and develop groundbreaking machine learning algorithms and systems.

Key Requirements:

To be considered for this Machine Learning Researcher role, you must have:

A PhD in Natural Language Processing, AI, Electrical Engineering, or a related field.
Experience with ML frameworks such as PyTorch, TensorFlow, or JAX.
Strong programming skills in C++, C, or Python.
Experience working with embedded or mobile devices.
Ideally, 2+ years of industry experience post-PhD.

How to Apply:

To apply, please send your CV to (url removed) or contact Nick on (phone number removed) / (phone number removed)

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.

New Machine Learning Employers to Watch in 2026: UK and Global Companies Driving ML Innovation

Machine learning (ML) has transitioned from a specialised field into a core business capability. In 2026, organisations across healthcare, finance, robotics, autonomous systems, natural language processing, and analytics are expanding their machine learning teams to build scalable intelligent products and services. For professionals exploring opportunities on www.MachineLearningJobs.co.uk , understanding the companies that are scaling, winning investment, or securing high‑impact contracts is crucial. This article highlights the new and high‑growth machine learning employers to watch in 2026, focusing on UK innovators, international firms with significant UK presence, and global platforms investing in machine learning talent locally.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.