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For Companies At a Glance
- Tasks: Own and develop production biomarker methods using cutting-edge ML techniques.
- 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 by turning complex data into actionable insights.
- Qualifications: MSc/PhD in relevant fields with strong Python and ML skills.
- 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 Slough 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 Slough
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups or webinars, and connect with professionals 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 real-world problems. Practice explaining complex concepts in simple terms for non-technical folks.
✨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 background aligns with our mission in advancing cancer research and drug development.
We think you need these skills to ace Senior ML Engineer( Computational Biology) in Slough
Some tips for your application 🫡
Show Your Passion for Biology: Make sure to highlight your interest in biology and how it ties into your machine learning expertise. 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.
Tailor Your Experience: When detailing your experience, focus on projects that align with our needs, like working with omics data or developing deep learning models. We love seeing how your background fits into the role, so don’t hold back!
Be Clear and Concise: In your application, clarity is key! Use straightforward language to explain your skills and experiences. We appreciate a well-structured application that gets straight to the point without unnecessary fluff.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy to do!
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 where you've designed and implemented ML pipelines. Highlight any production systems you've worked on, especially those that turned complex data into actionable insights. Real-world examples will make you stand out.
✨Understand the Role
This isn't just about research; it's about ownership and reliability in production. Be ready to explain how you would approach turning biological data into robust biomarkers and how you'd handle challenges along the way. Show them you’re not just a theorist but a doer.
✨Communicate Clearly
You’ll need to explain complex concepts to both technical and non-technical stakeholders. Practice articulating your thoughts on trade-offs in methods and why certain approaches are chosen over others. Clear communication can set you apart from other candidates.
