Machine Learning Scientist

Hyper Recruitment Solutions
Cambridge
9 months ago
Applications closed

Related Jobs

View all jobs

Machine Learning Scientist

Senior Machine Learning Scientist

Senior Machine Learning Scientist

Principal Machine Learning Scientist - Applied Research (UK Remote)

Senior Machine Learning Scientist

Senior Machine Learning Scientist

ROLE OVERVIEW:


We are currently looking for a Machine Learning Scientist to join a leading pharmaceutical company based in the Cambridge area. As the Machine Learning Scientist, you will be responsible for driving the development of innovative machine learning methods for structure-based drug discovery.


KEY DUTIES AND RESPONSIBILITIES:


Your duties as the Machine Learning Scientist will be varied however the key duties and responsibilities are as follows:


1. Design and implement ML models for structure-based design, including protein-ligand interaction modelling and co-folding applications.


2. Develop and extend AI approaches that integrate structural and chemical data to improve virtual screening and molecular design workflows.


3. Leverage proprietary structural datasets to train, benchmark, and validate new algorithms.


4. Collaborate closely with cross-functional teams to ensure effective translation of research into production-ready solutions.


ROLE REQUIREMENTS:


To be successful in your application to this exciting role as the Machine Learning Scientist we are looking to identify the following on your profile and past history:


1. Relevant degree in a technical discipline (e.g., computer science, chemistry, physics, engineering).


2. Proven industry experience in machine learning, including deep learning and/or generative models.


3. A working knowledge and practical experience with modern ML frameworks (e.g., PyTorch, TensorFlow, or JAX).


Key Words:

Machine Learning / Drug Discovery / Computational Chemistry / Structural Biology / AI Techniques / Protein-Ligand Interaction / Molecular Modelling / Virtual Screening / Deep Learning / Generative Models


Hyper Recruitment Solutions Ltd (HRS) is an Equal Opportunities employer. We welcome applications from anyone who meets the role requirements. HRS exclusively supports the Life Science sectors, combining recruitment expertise with scientific knowledge to help you advance your career.

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 Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

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

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.