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Research Scientist; Machine Learning

Job in City Of London, Central London, Greater London, England, UK
Listing for: BioTalent Ltd
Part Time position
Listed on 2025-12-28
Job specializations:
  • Research/Development
    Data Scientist, Artificial Intelligence
  • IT/Tech
    Data Scientist, Machine Learning/ ML Engineer, Artificial Intelligence, AI Engineer
Salary/Wage Range or Industry Benchmark: 80000 - 100000 GBP Yearly GBP 80000.00 100000.00 YEAR
Job Description & How to Apply Below
Position: Research Scientist (Machine Learning)
Location: City Of London

Overview

We’re looking for a
Research Scientist (Machine Learning) to join an ambitious and interdisciplinary team applying AI to transform drug discovery and accelerate the development of life-changing medicines.

This is a chance to work on cutting-edge ML research with direct real-world impact, in a collaborative environment where innovation and creativity are encouraged.

This role will off you

  • Work at the intersection of AI and life sciences with high-impact applications.
  • Hybrid working (3 days per week in the London office).
  • A collaborative, inclusive culture with opportunities for growth and leadership.
What you’ll do
  • Design and develop novel ML models and algorithms.
  • Apply deep learning and generative modelling to complex scientific problems.
  • Collaborate with experts across biology, chemistry, physics, and engineering.
  • Analyse, tune, and optimise experimental results.
  • Depending on experience: lead projects, mentor others, and shape research strategy.
What you’ll bring
  • PhD (or equivalent experience) in ML, computer science, or a related field.
  • Proven expertise in deep learning research and model development.
  • Strong knowledge of mathematics (linear algebra, calculus, statistics).
  • Experience with ML frameworks such as PyTorch, Tensor Flow, or JAX.
  • A passion for applying ML to real-world scientific challenges.
Nice to have
  • Experience working with biological or chemical data.
  • Familiarity with large-scale deep learning, generative models, GNNs, RL, or computer vision.
  • Contributions to publications, research projects, or open-source ML.
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