Principal Machine Learning Engineer, Structural Biology | Pharma/BioTech | Series A - Drug discovery B2B Platform | Fully Remote, EU | £ 850-1200pd, Outside IR35 | 6-12 months Contract Length
The Client:A leading organization in the drug discovery field is currently looking for aPrincipal ML Engineerto spearhead the technical direction for their structural biology models. This hands-on, high-impact role offers the opportunity to advance the application of foundational models to complex structural biology challenges.
The successful candidate will work closely with the leadership team, serving as the technical authority on machine learning modeling, architecture, and experimentation in this domain. While this role does not involve people management, the candidate will be expected to provide mentorship and guidance to engineers and researchers on technical content.
The ideal candidate will have deep expertise in training and deploying transformer-based models for protein structure prediction and related tasks. Additionally, they should have a strong understanding of how these models are applied within drug discovery workflows. A proven track record in setting strategy, solving complex technical problems, and delivering impactful ML systems is essential.
Responsibilities include:
- Define approaches for data preprocessing, selection, and benchmarking for training tasks involving protein structures, complexes, and multimodal biological datasets.
- Design and implement model extensions tailored to challenges like predicting protein complex interactions and binding affinities, including data processing, benchmarking, and evaluation pipelines.
- Mentor and guide team members, assisting in complex project planning and execution related to structural biology modeling.
- Lead the technical strategy for machine learning applications in structural biology, focusing on adapting foundational models such as those for protein folding.
- Influence decisions on model architecture, data infrastructure, and deployment strategies.
- Collaborate with other teams to ensure models meet practical scientific discovery needs.
- Contribute to scientific publications or open-source projects when applicable.
- Develop and maintain scalable, production-ready ML systems, including training, inference, and deployment pipelines.
Expected Milestones:
- By month 3: Lead a structural biology modeling project with a strategy and experiment plan for foundational model adaptation.
- By month 6: Deliver the initial model extension with benchmarking and a reproducible pipeline.
- By month 12: Oversee multiple ML initiatives, demonstrating improvements and providing mentorship.
Qualifications:
- PhD or equivalent in machine learning, computational biology, or structural biology with proven application experience.
- Extensive experience with transformer-based models (e.g., protein folding models) using frameworks like PyTorch.
- Understanding of data challenges in structural biology and scalable workflows.
- Experience with ML systems at scale, including CI/CD, versioning, and distributed GPU training.
- Proficiency with MLOps tools like Docker, Kubernetes, and cloud platforms.
- Ability to navigate complex technical environments and execute ambitious projects.
- Knowledge of how structural biology models impact drug discovery and ability to align work with applications.
If you are a good fit for this role, please send your CV, and we will contact you if your profile matches our needs.
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