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- Tasks: Apply machine learning to molecular science and drug discovery projects.
- Company: Tech-focused company revolutionising scientific research with ML.
- Benefits: Competitive salary, equity participation, and flexible working options.
- Why this job: Make a real impact in science using cutting-edge machine learning techniques.
- Qualifications: Experience in machine learning, Python, and interdisciplinary collaboration.
- Other info: Join a dynamic team with high ownership over your work.
The predicted salary is between 36000 - 60000 £ per year.
Location: London / Flexible (Hybrid or Remote)
Contract: Full-time
About the organisation
We are a technology-focused company applying modern machine-learning techniques to problems in molecular science and early-stage drug discovery. Our goal is to translate recent advances in ML into practical tools that support scientific research and decision-making in real-world discovery programmes. The team brings together experience in machine learning, software engineering, and the life sciences, and collaborates closely with internal scientific teams and external research partners.
The role
This role focuses on applying machine-learning methods to scientific and molecular datasets, with an emphasis on building robust, usable solutions rather than purely exploratory research. You will work across the full lifecycle of applied modelling projects, from understanding scientific objectives through to model development, evaluation, and integration into downstream workflows. You will collaborate with researchers and engineers across multiple disciplines, contributing both technical expertise and practical insight into how ML systems perform in applied scientific settings.
Key responsibilities
- Apply machine-learning techniques to problems in molecular modelling and computational life sciences
- Design and execute modelling experiments, including dataset preparation, model training, fine-tuning, and evaluation
- Translate scientific questions into well-defined modelling approaches
- Contribute to the development of maintainable, reproducible ML codebases
- Work closely with scientists, engineers, and other stakeholders to support research objectives
- Assess model performance and identify areas for improvement in applied settings
Requirements
- Hands-on experience applying machine-learning methods to real-world problems
- Experience working with biological, chemical, or scientific data
- Strong proficiency in Python and common ML frameworks (e.g. PyTorch)
- Experience writing and maintaining production-quality or research-grade ML code
- Experience working in interdisciplinary teams
- Exposure to collaborative projects with external partners
- Background in applied or translational ML research
What’s on offer
- Opportunity to work on applied ML problems in a scientific discovery context
- High degree of ownership over technical work
- Collaboration with a multidisciplinary technical and scientific team
- Competitive compensation package, including equity participation
Applied Machine Learning/ Scientist employer: S3 Science Recruitment
Contact Detail:
S3 Science Recruitment Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Applied Machine Learning/ Scientist
✨Tip Number 1
Network like a pro! Reach out to professionals in the machine learning and life sciences fields on LinkedIn. Join relevant groups and participate in discussions to get your name out there and show off your expertise.
✨Tip Number 2
Prepare for interviews by brushing up on your technical skills and understanding the latest trends in ML. Practice explaining complex concepts in simple terms, as you'll need to communicate effectively with both scientists and engineers.
✨Tip Number 3
Showcase your projects! Create a portfolio that highlights your hands-on experience with machine learning methods. Include examples of how you've tackled real-world problems, especially those related to molecular science or drug discovery.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Applied Machine Learning/ Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the role of Applied Machine Learning Scientist. Highlight your hands-on experience with machine-learning methods and any relevant projects you've worked on, especially those involving biological or chemical data.
Craft a Compelling Cover Letter: Your cover letter should tell us why you're passionate about applying ML in molecular science. Share specific examples of how you've tackled real-world problems using ML techniques and how you can contribute to our team.
Showcase Your Technical Skills: Don’t forget to mention your proficiency in Python and any ML frameworks like PyTorch. We want to see your technical expertise shine through, so include any relevant projects or code samples if possible!
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 shows you’re keen on joining our team!
How to prepare for a job interview at S3 Science Recruitment
✨Know Your ML Stuff
Make sure you brush up on your machine learning techniques, especially those relevant to molecular science. Be ready to discuss specific projects where you've applied these methods, and how they contributed to solving real-world problems.
✨Showcase Your Coding Skills
Since strong proficiency in Python and ML frameworks like PyTorch is crucial, prepare to demonstrate your coding abilities. You might be asked to solve a problem on the spot or discuss your approach to writing maintainable ML code.
✨Understand the Science
Familiarise yourself with the basics of molecular modelling and computational life sciences. Being able to translate scientific questions into modelling approaches will show that you can bridge the gap between ML and scientific research effectively.
✨Collaboration is Key
Highlight your experience working in interdisciplinary teams. Be prepared to share examples of how you've collaborated with scientists and engineers, and how you’ve contributed to achieving shared research objectives.
