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- Tasks: Design and implement ML methods for genomics and biological interpretation.
- Company: Innovative biotech firm focused on cancer research and drug development.
- Benefits: Remote work, competitive salary, and opportunities for professional growth.
- Why this job: Make a real impact 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 City of 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 City of London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people 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 and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on both technical and soft skills. Be ready to discuss your experience with ML pipelines and how you've tackled challenges in past projects. Practice makes perfect!
✨Tip Number 4
Don’t forget to apply through our website! We love seeing candidates who are genuinely interested in joining us at StudySmarter. Tailor your application to highlight how your skills align with our mission in advancing cancer research.
We think you need these skills to ace Senior ML Engineer( Computational Biology) in City of 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 about the tech, but that you genuinely care about using it to advance cancer research and drug development.
Be Specific About Your Experience: When detailing your past work, focus on specific projects where you’ve designed and implemented ML pipelines or worked with omics data. We love seeing concrete examples of how you’ve turned complex data into actionable insights.
Demonstrate Your Technical Skills: Don’t hold back on showcasing your Python prowess and any hands-on experience with deep learning models. Mention any familiarity with tools like Git, Docker, or CI/CD practices, as these are crucial for our production environment.
Tailor Your Application: Make your application stand out by tailoring it to our job description. Use similar language and focus on the aspects of your experience that align with what we’re looking for. And remember, applying through our website is the best way to get noticed!
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 and how you ensured their reliability. Use concrete examples to illustrate your problem-solving skills and your ability to turn complex data into actionable insights.
✨Understand the Role's Impact
This role is all about turning biological data into meaningful biomarkers. Be ready to discuss how your work can directly impact cancer research and drug development. Show that you understand the importance of reproducibility and robustness in your methods.
✨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 be prepared to answer questions about your decision-making process. Clear communication can set you apart from other candidates!
