At a Glance
- Tasks: Build and apply AI models for enzyme catalysis in pharma, agriculture, and industry.
- Company: Dayhoff Labs, a pioneering company in AI-driven biochemistry.
- Benefits: Competitive salary, visa sponsorship, and opportunities to work in London or Manchester.
- Other info: Collaborative environment with strong focus on innovation and practical applications.
- Why this job: Make a real impact by solving complex problems with cutting-edge technology.
- Qualifications: Experience in computational design and fluency in protein ML and biocatalysis fundamentals.
The predicted salary is between 60000 - 80000 £ per year.
Dayhoff Labs reverse engineers the chemistry of life using AI and experiments. In this role, you will build and apply foundational models of enzyme catalysis with use cases in pharma manufacture, agriculture and industry. As a member of the AI team, you will build, maintain and use frontier AI models on real world problems together with our partners in industry and academia.
The role is pragmatic computational enzyme engineering. You will both build and use internal models, and work with and fine tune external models like Boltz, RFdiffusion and LigandMPNN. You’ll also be happy to use classical biocatalysis methods where appropriate. The role is to augment both discovery and optimization of enzymes, working closely with our wet lab engineers and foundation model team to deliver solutions for the biocatalysis community.
What you’ll actually be doing:
- Owning the computational side of one to three commercial projects at a time, end to end: substrate analysis, starting-point selection, optimisation strategy, design rounds, in silico characterisation.
- Picking the tool stack per project. There is no fixed pipeline. You defend your choices on technical grounds and revise them when the data says otherwise.
- Designing and training novel architectures.
- Acquiring new data for training both computationally and experimentally.
- Working closely with the wet-lab team on assay design, hit-call thresholds, and iteration.
What we’re looking for:
- A track record of putting computational designs into wet labs and tracking what happened. The ratio of worked-to-didn’t-worked is less important than whether you can explain the failures mechanistically.
- Fluency across the protein ML stack (At least some of ESM, AlphaFold or Boltz, RFdiffusion, ProteinMPNN / LigandMPNN, docking) and a similar fluency in biocatalysis fundamentals (mechanism, kinetics, common cofactors, expression bottlenecks).
- Good taste in tool selection.
- Comfort talking to chemists and fermentation engineers about your model choices in their language.
- A sceptical disposition toward your own outputs. You will tell us when a prediction is junk and you will not over-claim.
Practicalities:
- Very competitive compensation. The role could be based in London or Manchester UK. We sponsor visas where the case is strong.
To apply:
Send a CV and a short note — no cover-letter format required — describing the most recent computational design you put into a wet lab, what happened, and what you would do differently. Email careers@dayhofflabs.com with subject line "Computational Scientist, Biocatalysis."
Computational Scientist, Biocatalysis employer: Dayhoff Labs
At Dayhoff Labs, we pride ourselves on being an innovative employer that fosters a collaborative and dynamic work culture. Our London and Manchester locations offer competitive compensation, opportunities for professional growth, and the chance to work at the forefront of AI and biocatalysis, making a tangible impact in pharma, agriculture, and industry. Join us to be part of a team that values creativity, critical thinking, and the pursuit of scientific excellence.
StudySmarter Expert Advice🤫
We think this is how you could land Computational Scientist, Biocatalysis
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those at Dayhoff Labs. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Prepare a mini-project or case study related to enzyme catalysis. This will demonstrate your hands-on experience and problem-solving abilities.
✨Tip Number 3
Be ready for technical discussions! Brush up on your knowledge of protein ML stacks and biocatalysis fundamentals. You want to impress them with your fluency in their language.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in joining the team at Dayhoff Labs.
We think you need these skills to ace Computational Scientist, Biocatalysis
Some tips for your application 🫡
Be Yourself:When you're writing your application, let your personality shine through! We want to see the real you, so don’t be afraid to show your passion for computational science and biocatalysis. Authenticity goes a long way!
Focus on Your Experience:Make sure to highlight your relevant experience in computational designs and wet lab applications. We’re interested in what you’ve done recently, so share specific examples that showcase your skills and how they relate to the role.
Keep It Concise:We appreciate brevity! Your note should be short and to the point. Focus on the most recent project you worked on, what you learned, and how you’d approach it differently next time. Clarity is key!
Apply Through Our Website:Don’t forget to send your application via our website! It’s the best way to ensure it gets to us. Use the subject line 'Computational Scientist, Biocatalysis' when emailing us at careers@dayhofflabs.com to keep things organised.
How to prepare for a job interview at Dayhoff Labs
✨Know Your Models Inside Out
Make sure you’re well-versed in the foundational models of enzyme catalysis mentioned in the job description. Be ready to discuss your experience with tools like ESM, AlphaFold, and Boltz. Prepare examples of how you've applied these models in real-world scenarios.
✨Speak Their Language
Since you'll be collaborating with chemists and fermentation engineers, brush up on the terminology they use. Practise explaining your computational choices in a way that resonates with their expertise. This will show that you can bridge the gap between computational science and practical application.
✨Embrace Failure as a Learning Tool
The role values a track record of learning from failures. Be prepared to share specific instances where your computational designs didn’t work out as planned. Discuss what you learned and how you would approach it differently next time. This shows a growth mindset and a sceptical disposition towards your own outputs.
✨Prepare for Technical Questions
Expect to defend your tool selection and optimisation strategies during the interview. Think about the rationale behind your choices and be ready to discuss how you would adapt based on data. This demonstrates your critical thinking skills and adaptability in a dynamic environment.
