At a Glance
- Tasks: Lead the development of machine learning systems for drug discovery and mentor a growing team.
- Company: Early-stage biotech startup in Oxford focused on innovative drug discovery solutions.
- Benefits: Competitive salary, equity options, and a chance to shape the future of healthcare.
- Other info: Join a small, elite team with no bureaucracy and plenty of autonomy.
- Why this job: Make a real-world impact by transforming biological data into life-saving therapies.
- Qualifications: Strong ML background with experience in life sciences and production-level systems.
The predicted salary is between 100000 - 120000 € per year.
I'm working with an early-stage biotech startup based in Oxford building machine learning-driven platforms for drug discovery. Their mission is to dramatically reduce the time and cost of bringing new therapies to market by combining computational biology, large-scale biological datasets, and modern ML techniques. Backed by top-tier investors, they're a small, high-calibre team of scientists and engineers.
We're hiring a Lead Bio-ML Engineer to take ownership of our core machine learning systems. This is a high-impact, high-autonomy role where you'll:
- Own the end-to-end ML lifecycle (data, modelling, deployment)
- Work closely with biologists to translate experimental problems into ML solutions
- Build and scale models across areas like sequence modelling, structure prediction, and multimodal biological data
- Shape the technical direction and ML roadmap
- Mentor and grow a small team as they scale
What You'll Do:
- Designing models for genomics, proteomics, and drug discovery pipelines
- Applying deep learning / foundation models to biological datasets
- Building robust, production-ready ML systems (not just research prototypes)
- Developing internal tooling for data pipelines, experiment tracking, and model evaluation
Core experience:
- Strong background in machine learning / AI engineering
- Experience applying ML to life sciences, bioinformatics, or computational biology
- Production experience with Python + PyTorch / TensorFlow
- Track record of owning and shipping ML systems end-to-end
Nice to have:
- Experience with biological data (e.g. sequencing, protein structures)
- Familiarity with drug discovery or wet-lab collaboration
- Exposure to LLMs / foundation models in biology
- Previous startup or high-ownership environment
High ownership: you'll define and build critical systems from the ground up
Real-world impact: your work directly contributes to therapeutic discovery
Tight feedback loop: collaborate daily with domain experts
Equity upside: meaningful stake in an early-stage company
Small, elite team: no bureaucracy, just execution
Please apply here or reach out to me directly on LinkedIn/ via email (adam.lockett@sr2rec.co.uk).
Lead Bio Machine Learning Engineer in Oxford employer: LinkedIn
As an early-stage biotech startup in Oxford, we offer a unique opportunity for the Lead Bio Machine Learning Engineer to make a significant impact in drug discovery through innovative machine learning solutions. Our collaborative work culture fosters close partnerships with biologists, ensuring that your contributions directly influence therapeutic advancements. With competitive compensation, equity options, and a focus on employee growth within a small, elite team, we provide an environment where your expertise can thrive and evolve.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Bio Machine Learning Engineer in Oxford
✨Tip Number 1
Network like a pro! Reach out to people in the biotech and machine learning fields on LinkedIn. Join relevant groups and engage in discussions to get your name out there and show your passion for the industry.
✨Tip Number 2
Prepare for those interviews! Research the company and its mission, especially their work in drug discovery. Be ready to discuss how your experience aligns with their goals and how you can contribute to their ML systems.
✨Tip Number 3
Showcase your projects! If you've worked on any relevant ML projects, make sure to highlight them during conversations. Whether it's genomics or proteomics, demonstrating your hands-on experience can set you apart from other candidates.
✨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 contributing to our mission.
We think you need these skills to ace Lead Bio Machine Learning Engineer in Oxford
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience in machine learning and bioinformatics. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter:Your cover letter is your chance to tell us why you’re the perfect fit for the Lead Bio-ML Engineer position. Share your passion for drug discovery and how your background can help us achieve our mission.
Showcase Your Technical Skills:We’re looking for someone with a strong background in Python and ML frameworks like PyTorch or TensorFlow. Be sure to mention any specific projects where you’ve built production-ready ML systems!
Apply Through Our Website:To make sure your application gets the attention it deserves, apply directly through our website. It’s the best way for us to keep track of your application and get back to you quickly!
How to prepare for a job interview at LinkedIn
✨Know Your ML Stuff
Make sure you brush up on your machine learning fundamentals, especially in the context of bioinformatics and computational biology. Be ready to discuss your experience with Python, PyTorch, and TensorFlow, as well as any specific projects where you've built production-ready ML systems.
✨Understand the Biotech Landscape
Familiarise yourself with the latest trends in drug discovery and how machine learning is being applied in this field. This will not only show your passion for the role but also help you engage in meaningful conversations with the interviewers about their mission and goals.
✨Prepare for Technical Questions
Expect to dive deep into technical discussions during the interview. Prepare to explain your approach to the end-to-end ML lifecycle, including data handling, model design, and deployment. Practising coding challenges or case studies related to biological datasets can give you an edge.
✨Show Your Leadership Skills
Since this role involves mentoring a small team, be prepared to discuss your leadership style and experiences. Think of examples where you've successfully guided others or taken ownership of projects, and be ready to share how you plan to shape the technical direction of the team.
