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- Tasks: Apply cutting-edge machine learning to real-world drug discovery challenges.
- Company: Mission-driven tech company focused on AI-powered molecular modelling.
- Benefits: Competitive salary, equity participation, and collaborative work environment.
- Why this job: Make a real impact in science by developing advanced tools for researchers.
- Qualifications: Experience with machine learning, biological datasets, and proficiency in PyTorch.
- Other info: Join a multidisciplinary team and enjoy significant ownership of projects.
The predicted salary is between 36000 - 60000 £ per year.
We are a mission-driven technology company building advanced AI-powered tools for molecular modelling and drug discovery. Our work focuses on making modern machine-learning methods practical, scalable, and accessible for real scientific workflows in chemistry and biology. Our models and platforms are used by a broad range of researchers across biotech, pharma, and academia to support molecular design, evaluation, and decision-making in early-stage discovery programmes. We emphasise production-grade systems that integrate naturally into how scientists work, enabling faster iteration from biological hypotheses to candidate molecules.
As an Applied Machine Learning Engineer / Scientist, you will apply and adapt state-of-the-art machine-learning models to real-world problems in drug discovery and molecular design. You will work on applied projects in close collaboration with external partners, focusing on tailoring advanced models to specific scientific objectives. Your role will involve translating partner and internal research needs into concrete modelling strategies. This includes curating and adapting datasets, selecting and tuning model architectures and objectives, running experiments, and iterating rapidly to maximise performance in applied settings. You will own the full applied modelling lifecycle, from problem formulation and experimentation through evaluation and delivery. You will also contribute insights from applied work back into core model development, helping identify gaps, limitations, and opportunities for improvement in foundational models based on real-world usage.
This role suits someone who enjoys operating at the intersection of cutting-edge ML and practical deployment — a technically strong, execution-focused scientist or engineer motivated by turning advanced models into reliable, high-impact tools.
About you
- Strong hands-on experience applying machine-learning methods to real-world problems
- Experience working with biological, chemical, or molecular datasets
- Strong proficiency with PyTorch and the scientific Python ecosystem (e.g. NumPy, SciPy, Pandas)
- Experience contributing to and maintaining high-quality deep-learning codebases, with attention to reproducibility, testing, and engineering standards
- Experience collaborating with external partners or stakeholders, translating scientific questions into concrete modelling and evaluation approaches
- Publication record in machine learning or life-science venues, particularly where research was driven by applied or translational goals
- Experience working in interdisciplinary environments spanning ML, biology, chemistry, and related fields
What’s on offer
- Opportunity to deliver real-world impact by building tools used by scientists across industry and academia
- Collaboration within a highly skilled, multidisciplinary technical and scientific team
- Significant ownership and autonomy across applied modelling projects
- Competitive compensation package, including meaningful equity participation
Machine Learning Research Engineer/Scientist employer: S3 Science Recruitment
Contact Detail:
S3 Science Recruitment Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Research Engineer/Scientist
✨Tip Number 1
Network like a pro! Reach out to professionals in the biotech and pharma sectors on LinkedIn. Join relevant groups and participate in discussions to get your name out there. We all know that sometimes it’s not just what you know, but who you know!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those related to molecular modelling or drug discovery. This will give potential employers a taste of what you can do. Don’t forget to share it when you apply through our website!
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice explaining complex concepts in simple terms, as you might need to translate scientific questions into modelling strategies. We want to see how you think on your feet!
✨Tip Number 4
Follow up after interviews! A quick thank-you email can go a long way in leaving a positive impression. It shows your enthusiasm for the role and keeps you fresh in their minds. Remember, we’re rooting for you to land that dream job!
We think you need these skills to ace Machine Learning Research Engineer/Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your hands-on experience with machine learning methods, especially in real-world applications. We want to see how your skills align with our mission in drug discovery and molecular design.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Share your passion for applying advanced ML models to practical problems and how you can contribute to our team. Let us know why you're excited about working at StudySmarter!
Showcase Your Projects: Include examples of projects where you've worked with biological or chemical datasets. We love seeing how you've translated scientific questions into modelling strategies, so don’t hold back on the details!
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 to join our mission-driven team!
How to prepare for a job interview at S3 Science Recruitment
✨Know Your Models Inside Out
Make sure you’re well-versed in the latest machine-learning models, especially those relevant to drug discovery and molecular design. Be ready to discuss how you've applied these models in real-world scenarios, and think about specific examples where your work made a tangible impact.
✨Showcase Your Collaboration Skills
Since this role involves working closely with external partners, be prepared to share experiences where you successfully collaborated on projects. Highlight how you translated scientific questions into modelling strategies and how you navigated any challenges that arose during these partnerships.
✨Demonstrate Technical Proficiency
Brush up on your skills with PyTorch and the scientific Python ecosystem. During the interview, be ready to discuss your experience with datasets, model tuning, and maintaining high-quality codebases. You might even be asked to solve a technical problem on the spot, so practice coding challenges beforehand!
✨Prepare Insightful Questions
Interviews are a two-way street! Prepare thoughtful questions about the company’s current projects, their approach to integrating ML into scientific workflows, and how they measure success. This shows your genuine interest in the role and helps you assess if it’s the right fit for you.
