Owen Thomas | B Corp™
Machine Learning Engineer | Alphafold, Boltz, Chai | Series A - Drug discovery Platform | Fully Remote, EU/UK | Base Salary Up to £130,000K, plus early equity+benefits
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Machine Learning Engineer | Alphafold, Boltz, Chai | Series A - Drug discovery Platform | Fully Remote, EU/UK | Base Salary Up to £130,000, plus early equity+benefits
The Client:
A mission-driven technology company operating in the life sciences domain is seeking a Machine Learning Engineer - hands-on Alphafold, Boltz or Chai to lead the technical direction 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 collaborate closely with leadership and mentoring other engineers and researchers. While this is not a people management position, it offers significant influence over technical direction.
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Responsibilities:
- Lead the design and implementation of ML solutions to deploy on large pharma datasets.
- Develop and extend models for specific applications, including data distillation, benchmarking, and evaluation.
- Define preprocessing and harmonisation strategies for diverse assay datasets.
- 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.
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Experience needed:
- PhD computational chemistry or equivalent
- Drug discovery experience
- Hands on experience building an ML based model on public and/or internal pharma datasets in the computational chemistry space.
- 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:
- Fully Remote Working Culture
- Up to £130,000 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!
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