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

JR United Kingdom; Leigh; Remote; GBP 136,000 - 160,000; Full time

Job summary

A leading organization in drug discovery seeks a Principal Machine Learning Engineer to advance structural biology models. This fully remote role involves mentoring, technical leadership, and collaboration on innovative ML systems, offering a competitive salary and equity options.

Benefits

Fully Remote Working Culture

Attractive Stock Options

Flexible hours

Qualifications

• PhD or equivalent experience in relevant fields.
• Extensive experience in building transformer-based models.

Responsibilities

• Define approaches for data preprocessing and benchmarking.
• Lead the technical strategy for machine learning applications.

Skills

Machine Learning

Structural Biology

Mentorship

Data Preprocessing

Model Architecture

Collaboration

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, Leigh, Greater Manchester

Client:

Location:

Job Category:

Other

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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 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 new training tasks involving protein structures, complexes, and multimodal biological datasets.
• Design and implement extensions to models tailored to specific challenges, such as predicting protein complex interactions and binding affinities, including data processing, benchmarking, and evaluation pipelines.
• Provide mentorship and guidance to team members, assisting with the planning and execution of complex projects related to structural biology modeling.
• Lead the technical strategy for machine learning applications in structural biology, focusing on adapting and expanding foundational models such as those for protein folding and related tasks.
• Influence key decisions regarding model architecture, data infrastructure, and model deployment strategies.
• Work collaboratively with other teams to ensure models address practical needs in scientific discovery.
• Contribute to scientific publications or open-source projects where applicable.
• Develop and maintain scalable, production-ready machine learning systems, including pipelines for training, inference, and deployment.

Expected Milestones:

• By month 3: Take charge of a structural biology modeling project. Create a strategy and experiment plan for adapting foundational models to a high-value application.
• By month 6: Deliver the initial functional model extension (e.g., binding affinity prediction head), complete with benchmarking framework and a replicable pipeline.
• By month 12: Oversee multiple ML initiatives in structural biology, showcasing significant improvements in model accuracy and practical impact. Provide mentorship to peers and set the strategic direction for the area.

Qualifications:

• PhD (or equivalent experience) in machine learning, computational biology, or structural biology, with proven application in protein structure or drug discovery challenges.
• Extensive experience in building and training transformer-based models (e.g., protein folding models) using frameworks like PyTorch, PyTorch Lightning, or similar.
• Understanding of data challenges in structural biology and capability to design scalable workflows.
• Experience delivering ML systems at scale, including CI/CD pipelines, model versioning, and distributed GPU training.
• Proficiency with modern MLOps tools and infrastructure, such as Docker, Kubernetes, cloud platforms, and orchestration tools.
• Ability to navigate complex technical environments and execute ambitious modeling initiatives.
• Knowledge of how structural biology models contribute to drug discovery and aligning work with practical applications.

Remuneration includes:

• 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 believe you are a good fit for this role, send your CV, and we will contact you if your profile matches our needs.