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- Tasks: Lead innovative ML solutions for drug discovery while ensuring data privacy and security.
- Company: Mission-driven tech company in life sciences, focused on collaborative model development.
- Benefits: Up to £160,000 salary, equity options, flexible hours, and fully remote work.
- Other info: Join a dynamic team with significant influence over technical direction.
- Why this job: Make a real impact in drug discovery with cutting-edge technology and research.
- Qualifications: Experience in ML, federated learning, and a strong publication record.
The predicted salary is between 160000 - 160000 £ 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 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'll 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:
- Hands-on experience with co-folding and structure-based models.
- Deep knowledge of federated learning and the nuances of privacy risk in distributed environments.
- You build experiments to prove (or disprove) privacy claims using quantitative and qualitative data.
- You own the "messy" problems and can explain the why behind technical risks to non-technical leaders.
- A strong publication record in ML or Computational Biology.
- Experience working within industry consortia or complex partnerships.
- Past success influencing industry standards or regulatory privacy positions.
Remuneration:
- 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 think you are a good match for the role, send us your CV and if we think you are a good match, we will give you a call!
Principal Machine Learning Researcher (Privacy/Risk) | | Series A - Drug discovery Platform | Fully Remote, EU/UK | Base Salary Up to £160,000K, plus early equity+benefits in City of London 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 Researcher (Privacy/Risk) | | Series A - Drug discovery Platform | Fully Remote, EU/UK | Base Salary Up to £160,000K, plus early equity+benefits in City of London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those who work at companies you're interested in. A friendly chat can open doors and give you insights that a job description just can't.
✨Tip Number 2
Show off your skills! If you've got projects or research that align with the role, make sure to highlight them in conversations. Bring your portfolio to life by discussing how your work relates to their needs.
✨Tip Number 3
Prepare for the interview like it's a big presentation. Research the company’s mission and values, and think about how your experience fits into their goals. We want to see that you’re not just a fit on paper but also a cultural match!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in joining our team and making an impact in the drug discovery space.
We think you need these skills to ace Principal Machine Learning Researcher (Privacy/Risk) | | Series A - Drug discovery Platform | Fully Remote, EU/UK | Base Salary Up to £160,000K, plus early equity+benefits in City of London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Principal Machine Learning Researcher role. Highlight your hands-on experience with ML solutions, especially in areas like ADMET and data privacy.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about drug discovery and how your background in machine learning can contribute to our mission. Be specific about your achievements and how they relate to the job description.
Showcase Your Publications: If you have a strong publication record, don’t forget to mention it! This is a great way to demonstrate your expertise in ML or Computational Biology, which is crucial for this role.
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, we love seeing candidates who take that extra step!
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 machine learning, especially in the context of ADMET and structural biology modelling. Be ready to discuss specific techniques like graph neural networks and transformers, as well as how they apply to drug discovery.
✨Showcase Your Privacy Expertise
Since this role focuses heavily on data privacy and risk, prepare to talk about your experience with federated learning and privacy risk management. Have examples ready that demonstrate how you've tackled privacy challenges in previous projects.
✨Prepare for Technical Questions
Expect to dive deep into technical discussions. Prepare to explain complex concepts in a way that non-technical leaders can understand. This will show your ability to communicate effectively across different levels of the organisation.
✨Highlight Collaboration Skills
This position involves working closely with leadership and mentoring others. Be ready to share examples of how you've successfully collaborated in past roles, particularly in complex partnerships or industry consortia.