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- Tasks: Drive the development of machine learning models for drug discovery and structural biology.
- Company: Leading drug discovery organisation with a focus on innovation and impact.
- Benefits: Salary up to £130,000, early equity, flexible remote work, and comprehensive benefits.
- Other info: Fully remote role with flexible hours and excellent career growth opportunities.
- Why this job: Make a real difference in drug discovery while working with cutting-edge technology.
- Qualifications: Experience in ML for protein structure prediction and strong problem-solving skills.
The predicted salary is between 130000 - 130000 £ per year.
A leading organization in the drug discovery field is currently looking for a client facing Senior Machine Learning Engineer to drive the development of foundational models that directly impact real-world drug discovery workflows. 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 brings deep expertise in training and deploying state-of-the-art models for protein structure prediction. Beyond technical proficiency, you must understand how these models integrate into broader drug discovery pipelines and possess the strategic mindset needed to break down complex problems into actionable, impactful ML solutions.
Requirements:- Proven experience building and training contemporary models (e.g., a strong track record of applying ML to real-world protein structure prediction or drug discovery problems).
- Comfortable in a fast-paced startup environment, with the ability to break down complex technical problems into impactful ML systems.
- Experience in Federated Learning, privacy-preserving ML, or a portfolio of publications in top-tier journals/conferences like NeurIPS, ICML, or Nature Methods.
- Work with our customers and academic partners to define data, preprocessing, selection, and benchmarking strategies for novel training tasks involving protein structures, complexes, and multimodal biological data.
- Carry out case-studies associated with the above, providing scientific and technical expertise to our customers. You will be involved in the full project pipeline, from scoping through to results delivery and dissemination.
- Advance the state-of-the-art by fine-tuning and customizing foundational architectures such as OpenFold, ESMFold, and Boltz-2 for specialized structural biology challenges.
- Architect model extensions tailored for binding affinity and protein complex prediction, overseeing everything from data distillation to rigorous benchmarking.
- Partner with leading academic and industry stakeholders to engineer data selection and preprocessing strategies for complex, multimodal biological datasets.
- Lead comprehensive technical case studies, managing the entire lifecycle from initial project scoping to the final dissemination of results.
- Develop and sustain high-scale ML pipelines that support efficient training, inference, and production-grade deployment.
- Drive external impact through high-quality open-source contributions and scientific publications.
Fully Remote Working Culture
B2B & Full time employee options
Flexible hours + - 3 hours of CET time zone
If you think you are a good match for the Machine Learning Engineer role, ping us over your CV and we will give you a call if we think you are a good match!
Senior Machine Learning Engineer, Structural Biology | Pharma/BioTech | Series A - Drug discovery B2B Platform | EU Remote | | Salary Up to £130,000K, plus early equity+benefits in City of London employer: Owen Thomas | B Corp™
Contact Detail:
Owen Thomas | B Corp™ Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Machine Learning Engineer, Structural Biology | Pharma/BioTech | Series A - Drug discovery B2B Platform | EU Remote | | Salary Up to £130,000K, plus early equity+benefits in City of London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those already working in drug discovery or machine learning. A friendly chat can lead to insider info about job openings and even referrals.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to protein structure prediction or ML models. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Be ready to discuss how you've tackled complex challenges in the past and how you can apply that experience to their specific needs.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love hearing from passionate candidates who are eager to make an impact in the field of drug discovery.
We think you need these skills to ace Senior Machine Learning Engineer, Structural Biology | Pharma/BioTech | Series A - Drug discovery B2B Platform | EU Remote | | Salary Up to £130,000K, plus early equity+benefits in City of London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with machine learning models, especially in structural biology. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects and achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about drug discovery and how your expertise can drive impactful solutions. Keep it engaging and personal – we love to see your personality come through!
Showcase Your Technical Skills: Be specific about the tools and technologies you’ve used in your previous roles. Mention any experience with foundational models or federated learning, as these are key to the position. We’re looking for depth, so don’t hold back!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it makes the process smoother for everyone involved!
How to prepare for a job interview at Owen Thomas | B Corp™
✨Know Your Models Inside Out
Make sure you can discuss your experience with training and deploying machine learning models, especially in the context of protein structure prediction. Be ready to explain how you've tackled complex problems and the impact your solutions had on drug discovery workflows.
✨Showcase Your Strategic Mindset
Prepare examples that demonstrate your ability to break down intricate technical challenges into actionable ML solutions. Highlight any experiences where you collaborated with stakeholders to define data strategies or worked on case studies that led to significant outcomes.
✨Familiarise Yourself with the Company’s Work
Research the organisation's current projects and their approach to foundational models in structural biology. Being able to discuss their work and how your skills align with their goals will show your genuine interest and help you stand out.
✨Prepare for Technical Questions
Expect to dive deep into technical discussions during the interview. Brush up on topics like Federated Learning and privacy-preserving ML, and be ready to share insights from your publications or experiences at top-tier conferences. This will showcase your expertise and commitment to the field.