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- Tasks: Design and deploy machine learning models for enzyme-powered chemistry.
- Company: Scindo, a pioneering company in sustainable chemistry.
- Benefits: Competitive salary, collaborative environment, and impactful work.
- Why this job: Join a fast-growing team and shape the future of chemistry with AI.
- Qualifications: PhD in relevant field and experience in machine learning for molecular systems.
- Other info: Central London location with excellent career growth opportunities.
The predicted salary is between 36000 - 60000 £ per year.
Scindo is building the next generation of enzyme-powered chemistry, combining wet-lab data with state-of-the-art machine learning. We are looking for a Machine Learning Scientist to design and deploy models that push the boundaries of enzyme prediction, reaction modelling, and generative catalyst design.
Responsibilities and requirements include:
- PhD (or equivalent) in Physics, Applied Mathematics, Computational Chemistry, or related field.
- Proven experience applying machine learning to molecular systems, e.g. protein engineering, enzyme catalysis, reaction prediction, molecular de novo design, molecular dynamics.
- Strong background working with deep learning architectures relevant to molecules/sequences:
-Transformers (e.g. ProtBERT, ESM, AlphaFold-like)
-Generative models (diffusion, VAEs, autoregressive) for proteins, molecules or materials.
- Hands-on experience with molecular dynamics and simulation data; familiarity with force fields, ab initio methods, or enhanced sampling.
- Strong programming in Python (PyTorch/TensorFlow, JAX, NumPy/SciPy); experience with scientific libraries such as RDKit, ASE, DeepChem.
- Experience with MLOps and end-to-end large-scale model development. (e.g. training, evaluation, benchmarking and deployment)
- Familiarity with vector databases and embeddings (Qdrant, Milvus, FAISS) for chemical/sequence similarity search.
- HPC/GPU cluster experience, performance optimisation, distributed training.
- Background in spectroscopy (IR/UV/Vis/NMR) and/or computational thermodynamics/kinetics.
- Exposure to enzyme engineering, biocatalysis, or structural biology data.
What we offer
- Opportunity to build a machine learning stack from the ground up, with direct impact on real-world sustainable chemistry.
- A highly collaborative lab–computational environment: every model prediction is tested in-house, feeding back into data pipelines.
- Central London lab/office with a fast-growing interdisciplinary team.
Seniority level: Mid-Senior level
Employment type: Full-time
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Machine learning scientist employer: Scindo
Contact Detail:
Scindo Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine learning scientist
✨Tip Number 1
Network like a pro! Reach out to professionals in the field of machine learning and chemistry on platforms like LinkedIn. Join relevant groups, attend webinars, and don’t be shy to ask for informational interviews – it’s all about making connections that could lead to job opportunities.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects related to enzyme prediction or molecular dynamics. Use GitHub to share your code and document your thought process. This not only demonstrates your expertise but also gives potential employers a glimpse into your problem-solving abilities.
✨Tip Number 3
Prepare for technical interviews by brushing up on your deep learning architectures and molecular systems knowledge. Practice coding challenges and be ready to discuss your past projects in detail. We recommend simulating interview scenarios with friends or using online platforms to get comfortable.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive and engaged with our mission in sustainable chemistry. So, go ahead and hit that apply button!
We think you need these skills to ace Machine learning scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in machine learning and molecular systems. We want to see how your skills align with our needs, so don’t be shy about showcasing relevant projects or research!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re excited about the role and how your background in enzyme-powered chemistry and machine learning makes you a perfect fit for us.
Showcase Your Technical Skills: We love seeing hands-on experience! Be sure to mention your programming skills in Python and any work you've done with deep learning architectures. Highlighting specific tools like PyTorch or TensorFlow can really make you stand out.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands and shows us you’re serious about joining our team!
How to prepare for a job interview at Scindo
✨Know Your Stuff
Make sure you brush up on your machine learning fundamentals, especially as they relate to molecular systems. Be ready to discuss your experience with deep learning architectures like Transformers and generative models, as well as any hands-on projects you've worked on in protein engineering or enzyme catalysis.
✨Showcase Your Coding Skills
Since strong programming skills in Python are a must, be prepared to demonstrate your proficiency. You might be asked to solve a coding problem on the spot, so practice using libraries like PyTorch or TensorFlow beforehand. Having examples of your work with scientific libraries like RDKit or DeepChem can really set you apart.
✨Talk About Collaboration
Scindo values a collaborative environment, so be ready to share examples of how you've worked effectively in teams. Discuss any interdisciplinary projects you've been involved in and how you’ve contributed to a shared goal, especially in lab-computational settings.
✨Prepare Questions
Interviews are a two-way street! Prepare thoughtful questions about Scindo's approach to enzyme-powered chemistry and their machine learning stack. This shows your genuine interest in the role and helps you assess if the company is the right fit for you.
