Principal Machine Learning Engineer, Structural Biology | Pharma/BioTech | Series A, Drug disco[...]

JR United Kingdom; Leeds; Remote; GBP 120,000 - 160,000; Full time

Job summary

A leading organization in drug discovery seeks a Principal Machine Learning Engineer specializing in structural biology. This fully remote position involves leading technical directions, mentoring, and developing scalable ML systems. Ideal candidates will have a PhD and extensive experience in transformer-based models, particularly in drug discovery applications.

Benefits

Fully remote work culture

Stock options

Flexible hours

Qualifications

• PhD or equivalent in relevant fields required.
• Extensive experience with transformer-based models is essential.
• Proficiency with MLOps tools and cloud infrastructure.

Responsibilities

• Define data preprocessing approaches for training tasks.
• Design model extensions for predicting protein interactions.
• Mentor team members on structural biology projects.

Skills

Machine Learning

Data Processing

Mentoring

Technical Strategy

Education

PhD in Machine Learning

PhD in Computational Biology

PhD in Structural Biology

Tools

PyTorch

Docker

Kubernetes

Job description

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Principal Machine Learning Engineer, Structural Biology | Pharma/BioTech | Series A, Drug discovery | Fully Remote, EU | Base Salary Up to £160,000, plus early equity+benefits, Leeds, West Yorkshire

Client:

Location:

Leeds, West Yorkshire, United Kingdom

Job Category:

Other

EU work permit required: Yes

Job Views: 3

Posted: 12.05.2025

Expiry Date: 26.06.2025

Job Description:

Principal Machine Learning Engineer, Structural Biology | Pharma/BioTech expertise | Series A - Drug discovery B2B Platform | Fully Remote, EU | Base Salary Up to £160,000, plus early equity+benefits

The Client:

A leading organization in the drug discovery field is currently looking for a Principal ML Engineer to lead the technical direction for their structural biology models. This high-impact, hands-on 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 mentor and guide 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, along with a strong understanding of their application within drug discovery workflows. A proven track record in strategy setting, 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 for challenges like predicting protein complex interactions and binding affinities, including data processing, benchmarking, and evaluation pipelines.
• Mentor and guide team members, assisting in planning and executing complex structural biology projects.
• Lead the technical strategy for ML 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 where applicable.
• Develop and maintain scalable, production-ready ML systems, including training, inference, and deployment pipelines.

Milestones:

• By month 3: Lead a structural biology modeling project, creating strategies and experimental plans for foundational models.
• By month 6: Deliver initial model extension with benchmarking and pipelines.
• 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, CI/CD pipelines, model versioning, and distributed GPU training.
• Proficiency with MLOps tools and cloud infrastructure such as Docker, Kubernetes.
• Ability to navigate complex technical environments and execute ambitious projects.
• Knowledge of how structural biology models contribute to drug discovery.

Remuneration:

• Fully remote work culture
• Up to £160,000 base salary
• Stock options
• B2B & full-time employment options
• Flexible hours, ±3 hours CET timezone

If you believe you're a good fit for the Principal Machine Learning Engineer, Structural Biology role, send your CV, and we will contact you if there's a match.