Job Specifications
Principal Machine Learning Scientist | ADMET/Structural Biology | Series A - Drug discovery Platform | Fully Remote, EU | Base Salary Up to £160,000K, plus early equity+benefits
The Client:
A mission-driven technology company operating in the life sciences domain is seeking a Principal Scientist - hands-on with either ADMET or Structural Biology modelling, ML engineer to lead the technical direction for ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) modeling efforts within its drug discovery platform. The organisation enables collaborative model development across partner organisations while maintaining strict data privacy and ownership, using a federated data infrastructure.
In this hands-on, high-impact role, you’ll work at the intersection of machine learning, computational chemistry, and applied research to advance foundational model applications in drug discovery. You'll be the technical authority on ML architecture, experimentation, and strategy, while focusing specifically on data security and privacy. You will also collaborateclosely with leadership and mentoring other engineers and researchers. While this is not a people management position, it offers significant influence over technical direction.
Responsibilities for the Principal Machine Learning Scientist | ADMET/Structural Biology | Series A - Drug discovery Platform | Fully Remote, EU | Base Salary Up to £160,000K, plus early equity+benefits
Lead the design and implementation of ML solutions for ADMET using cutting-edge techniques such as graph neural networks and transformers.
Lead the research and implimentation of data privacy within the models and establish privacy attack-surface assessment
Develop and extend models for specific applications, including data distillation, benchmarking, and evaluation.
Define preprocessing and harmonization strategies for diverse assay datasets used in ADMET modeling.
Author or contribute to scientific publications or open-source software where appropriate.
3 Month Plan:
Develop a working understanding of the product, federated training setup, and key life-sciences modelling use cases.
Reproduce and extend at least one existing modelling pipeline to establish a baseline privacy and attack-surface assessment.
Contribute to privacy analysis for one or more active federated drug discovery programs as they transition from setup into live operation.
Experience needed for the Principal Machine Learning Scientist | ADMET/Structural Biology | Series A - Drug discovery Platform | Fully Remote, EU | Base Salary Up to £160,000K, plus early equity+benefits
PhD computational chemistry or equivalent
Drug discovery experience (5y+)
Hands on ADMET or Structural Biology modelling experience (broad scope)
Hands on experience building an ML based model on public and/or internal pharma datasets in the computational chemistry space.
Strong understanding of OpenFold, AlphaFold, Boltz and co-folding
Confidence in building silico models
Excellent communicator and connecting stakeholders excellent planning capabilities for experimental planning and execution
Experience in working in consortium is a plus
Remuneration for the Principal Machine Learning Scientist ADMET / Structural Biology
Fully Remote Working Culture
Up to £160,000 Base Salary
Attractive Stock Options
B2B & Full time employee options
Flexible hours + - 3 hours of CET time zone
If you think you are a good match for the role, send us your CV and if we think you are a good match, we will give you a call!
About the Company
Hey, we are Owen Thomas. We are a relationship first recruitment partner for Data, Product & Engineering, across Contract and Permanent roles in the UK & Europe.
Humanness matters. We believe that empathy is the core of great recruitment; bringing opportunity and businesses to life, beyond what a person can see on a website.
All done with our OT personal touch.
Our mission: To be leaders within our specialisms and create unrivalled opportunities both internally and externally.
We balance purpose and profit. Actively in...
Know more