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Principal Machine Learning Researcher (Privacy/Risk) | | Series A – Drug discovery Platform | Fully Remote, EU/UK | 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 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 Researcher (Privacy/Risk) | | Series A – Drug discovery Platform | Fully Remote, EU/UK | 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 Researcher (Privacy/Risk) | | Series A – Drug discovery Platform | Fully Remote, EU/UK | Base Salary Up to £160,000K, plus early equity+benefits
- Hands-on experience with co-folding and structure-based models.
- Deep knowledge of federated learning and the nuances of privacy risk in distributed environments.
- You build experiments to prove (or disprove) privacy claims using quantitative and qualitative data.
- You own the “messy” problems and can explain the why behind technical risks to non-technical leaders.
- A strong publication record in ML or Computational Biology.
- Experience working within industry consortia or complex partnerships.
- Past success influencing industry standards or regulatory privacy positions.
Remuneration for the Principal Machine Learning Researcher (Privacy/Risk) | | Series A – Drug discovery Platform | Fully Remote, EU/UK | Base Salary Up to £160,000K, plus early equity+benefits
- 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!
