Principal Machine Learning Engineer

JR United Kingdom; United Kingdom; Remote; GBP 125,000 - 150,000; Full time

Job summary

A leading organization in the drug discovery field seeks a Principal Machine Learning Engineer to lead technical direction for structural biology models. This fully remote role offers the chance to work on impactful ML systems and mentor engineers in a high-impact environment.

Qualifications

• PhD or equivalent experience in relevant fields.
• Extensive experience in building transformer-based models.
• Experience delivering machine learning systems at scale.

Responsibilities

• Define data preprocessing approaches for training tasks.
• Design extensions to models for protein complex interactions.
• Lead technical strategy for machine learning applications.

Skills

Machine Learning

Data Preprocessing

Mentorship

Model Architecture

Education

PhD in Machine Learning

PhD in Computational Biology

PhD in Structural Biology

Tools

CI/CD Pipelines

Distributed GPU-based Training

Job description

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Job Views:

4

Posted:

12.05.2025

Expiry Date:

26.06.2025

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Job Description:

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

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. A proven track record in setting strategy, solving complex technical problems, and delivering impactful ML systems is essential.

Responsibilities include:

1. Define approaches for data preprocessing, selection, and benchmarking for new training tasks involving protein structures, complexes, and multimodal biological datasets.
2. 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.
3. Provide mentorship and guidance to team members, assisting with the planning and execution of complex projects related to structural biology modeling.
4. 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.
5. Influence key decisions regarding model architecture, data infrastructure, and model deployment strategies.
6. Work collaboratively with other teams to ensure models address practical needs in scientific discovery.
7. Contribute to scientific publications or open-source projects where applicable.
8. Develop and maintain scalable, production-ready machine learning systems, including pipelines for training, inference, and deployment.

Goals include:

1. By month 3: Take charge of a structural biology modeling project.
2. Oversee multiple ML initiatives in structural biology, showcasing significant improvements in model accuracy and practical impact.

Requirements:

• Hold a PhD (or equivalent experience) in machine learning, computational biology, or structural biology, with a proven track record of applying machine learning to real-world protein structure or drug discovery challenges.
• Extensive experience in building and training transformer-based models.
• Understanding of data challenges in structural biology and capability to design scalable workflows.
• Experience delivering machine learning systems at scale, including CI/CD pipelines, model versioning, and distributed GPU-based training.
• Knowledge of how structural biology models contribute to drug discovery and ability to align work with real-world applications.

If you think you are a good match for the Principal Machine Learning Engineer role, please send your CV for consideration.