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- Tasks: Lead the development of ML methods for genomics and cancer research.
- Company: Innovative biotech firm focused on impactful cancer research.
- Benefits: Remote work, competitive salary, and opportunities for professional growth.
- Why this job: Make a real difference in cancer research using cutting-edge machine learning.
- Qualifications: MSc/PhD in relevant fields and strong Python skills required.
- Other info: Collaborative environment with a focus on long-term method ownership.
The predicted salary is between 36000 - 60000 £ per year.
Location: Remote (UK-based, London time zone)
Type: Full-time
Focus: Genomics, single-cell biology, production ML systems
The Opportunity
We’re building production-grade computational methods that turn complex biological data into robust, interpretable biomarkers used daily to advance cancer research and drug development. This is not a purely exploratory research role. You’ll take true end-to-end ownership of methods that sit between raw omics data and biological interpretation — designing them, stress-testing them, and running them reliably in production. If you enjoy combining deep machine learning, real biological signal, and strong engineering practices, this role is built for you.
What You’ll Do
- Own production biomarker methods
- Design and implement genomics and transcriptomics pipelines (RNA-seq, single-cell, WGS/WES).
- Turn complex molecular data into scalable, reproducible biomarkers with clear assumptions and limitations.
- Continuously improve methods based on biological insight, feedback, and observed failure modes.
- Apply ML & AI to biological interpretation
- Develop and fine-tune deep learning models for biological representation learning (e.g. single-cell, multimodal data).
- Prototype AI-driven approaches (including LLMs and agentic workflows) for hypothesis generation and interpretation.
- Decide where ML meaningfully adds value — and where simpler methods are better.
- Evaluate emerging methods
- Track new approaches from literature and open source.
- Implement, benchmark, and critically assess robustness and generalisability.
- Drive adoption decisions based on evidence, not novelty.
What We’re Looking For
Background
- MSc / PhD (or equivalent industry experience) in Machine Learning, Computer Science, Computational Biology, Bioinformatics, or related field.
- Strong interest in biology and translational research; oncology exposure is a plus.
Technical profile
- Strong Python skills with experience building complex ML or data-processing pipelines.
- Hands-on experience with omics data (single-cell RNA-seq, bulk RNA-seq, WGS/WES, or multimodal genomics).
- Deep learning experience (e.g. transformers, VAEs, contrastive learning, GNNs).
- Familiarity with production-quality practices:
- Version control (Git)
- Reproducibility & testing
- Containerisation (Docker) and/or CI/CD
Mindset
- Enjoys owning methods long-term, not just publishing or prototyping.
- Comfortable working across biology, ML, and engineering.
- Able to clearly explain trade-offs to both technical and non-technical stakeholders.
Senior ML Engineer( Computational Biology) in London employer: Hlx Life Sciences
Contact Detail:
Hlx Life Sciences Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior ML Engineer( Computational Biology) in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to ML and computational biology. This gives potential employers a taste of what you can do beyond your CV.
✨Tip Number 3
Prepare for interviews by brushing up on both technical and biological concepts. Be ready to discuss your past projects and how they relate to the role. Practice explaining complex ideas simply!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Senior ML Engineer( Computational Biology) in London
Some tips for your application 🫡
Show Your Passion for Biology: Make sure to highlight your interest in biology and how it connects to your machine learning skills. We want to see that you’re not just a tech whiz, but someone who genuinely cares about using ML to advance cancer research and drug development.
Tailor Your Experience: When you’re writing your application, focus on the experiences that align with the role. We’re looking for specific examples of your work with omics data and deep learning models, so don’t hold back on those details!
Be Clear and Concise: We appreciate clarity! Make sure your application is easy to read and gets straight to the point. Use bullet points where necessary to break down your achievements and skills, especially when discussing complex projects.
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. Plus, it shows you’re serious about joining our team at StudySmarter.
How to prepare for a job interview at Hlx Life Sciences
✨Know Your Stuff
Make sure you brush up on your knowledge of genomics and single-cell biology. Be ready to discuss specific techniques like RNA-seq and WGS/WES, as well as how you've applied machine learning in these contexts. The more you can demonstrate your expertise, the better!
✨Showcase Your Projects
Prepare to talk about your previous projects involving ML pipelines and omics data. Highlight any production systems you've built or contributed to, and be ready to explain the challenges you faced and how you overcame them. Real-world examples will make you stand out.
✨Understand the Role's Impact
This role is all about turning complex data into actionable insights for cancer research. Be prepared to discuss how your work can directly impact drug development and patient outcomes. Showing that you understand the bigger picture will impress your interviewers.
✨Communicate Clearly
You'll need to explain technical concepts to both technical and non-technical stakeholders. Practice articulating your thoughts on trade-offs in ML methods and why certain approaches are better than others. Clear communication is key to success in this role!
