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- Tasks: Lead ML solutions for drug discovery, focusing on ADMET modelling and data privacy.
- Company: Mission-driven tech company in life sciences, fully remote and innovative.
- Benefits: Up to £160,000 salary, attractive stock options, and flexible remote work.
- Why this job: Make a real impact in drug discovery using cutting-edge machine learning techniques.
- Qualifications: PhD in computational chemistry and 5+ years in drug discovery with hands-on modelling experience.
- Other info: Collaborative environment with significant influence over technical direction and career growth.
The predicted salary is between 43200 - 72000 £ per year.
The Client: A mission-driven technology company operating in the life sciences domain is seeking a Principal Scientist - hands-on with either ADMET or Structural Biology modelling, ML engineer to lead the technical direction for ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) modeling efforts within its drug discovery platform. The organisation enables collaborative model development across partner organisations while maintaining strict data privacy and ownership, using a federated data infrastructure.
In this hands-on, high-impact role, you’ll work at the intersection of machine learning, computational chemistry, and applied research to advance foundational model applications in drug discovery. You will be the technical authority on ML architecture, experimentation, and strategy, while focusing specifically on data security and privacy. You will also collaborate closely with leadership and mentor other engineers and researchers. While this is not a people management position, it offers significant influence over technical direction.
Responsibilities:
- Lead the design and implementation of ML solutions for ADMET using cutting-edge techniques such as graph neural networks and transformers.
- Lead the research and implementation of data privacy within the models and establish privacy attack-surface assessment.
- Develop and extend models for specific applications, including data distillation, benchmarking, and evaluation.
- Define preprocessing and harmonization strategies for diverse assay datasets used in ADMET modeling.
- Author or contribute to scientific publications or open-source software where appropriate.
3 Month Plan:
- Develop a working understanding of the product, federated training setup, and key life-sciences modelling use cases.
- Reproduce and extend at least one existing modelling pipeline to establish a baseline privacy and attack-surface assessment.
- Contribute to privacy analysis for one or more active federated drug discovery programs as they transition from setup into live operation.
Experience needed:
- PhD in computational chemistry or equivalent.
- Drug discovery experience (5y+).
- Hands-on ADMET or Structural Biology modelling experience (broad scope).
- Hands-on experience building an ML-based model on public and/or internal pharma datasets in the computational chemistry space.
- Strong understanding of OpenFold, AlphaFold, Boltz and co-folding.
- Confidence in building silico models.
- Excellent communicator and connecting stakeholders.
- Excellent planning capabilities for experimental planning and execution.
- Experience in working in consortium is a plus.
Remuneration:
- Up to £160,000 Base Salary.
- Attractive Stock Options.
- B2B.
Principal Machine Learning Scientist | ADMET/Structural Biology | Series A - Drug discovery Platform | Fully Remote, EU | Base Salary Up to £160,000K, plus early equity+benefits employer: Owen Thomas | Pending B Corp™
Contact Detail:
Owen Thomas | Pending B Corp™ Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Principal Machine Learning Scientist | ADMET/Structural Biology | Series A - Drug discovery Platform | Fully Remote, EU | Base Salary Up to £160,000K, plus early equity+benefits
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend virtual meetups, and connect on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to ADMET or Structural Biology. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice common interview questions and be ready to discuss your experience with ML models and data privacy strategies.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got some fantastic opportunities waiting for you, and applying directly can sometimes give you an edge over other candidates.
We think you need these skills to ace Principal Machine Learning Scientist | ADMET/Structural Biology | Series A - Drug discovery Platform | Fully Remote, EU | Base Salary Up to £160,000K, plus early equity+benefits
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to highlight your experience in ADMET or Structural Biology modelling. 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 background in machine learning can contribute to our mission. Keep it engaging and personal – we love a good story!
Showcase Your Technical Skills: Don’t forget to highlight your hands-on experience with ML techniques like graph neural networks and transformers. We’re looking for someone who can lead the technical direction, so make sure we see your expertise front and centre!
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. Plus, we can’t wait to hear from you!
How to prepare for a job interview at Owen Thomas | Pending B Corp™
✨Know Your Stuff
Make sure you brush up on your knowledge of ADMET and Structural Biology modelling. Be ready to discuss specific techniques like graph neural networks and transformers, as well as your hands-on experience with them. The more you can demonstrate your expertise, the better!
✨Showcase Your Problem-Solving Skills
Prepare to talk about past projects where you've tackled complex problems in drug discovery. Highlight how you approached challenges related to data privacy and model implementation. Use concrete examples to illustrate your thought process and technical direction.
✨Communicate Clearly
As a Principal Machine Learning Scientist, you'll need to connect with various stakeholders. Practice explaining complex concepts in simple terms. This will show that you can effectively communicate your ideas and collaborate with others, which is crucial for this role.
✨Have a 3-Month Plan Ready
Think about how you would approach the first three months in the role. Outline your plan to understand the product, set up federated training, and contribute to privacy analysis. This shows initiative and gives the interviewers confidence in your strategic thinking.