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- Tasks: Own and develop production biomarker methods for cancer research using ML.
- Company: Innovative biotech firm focused on genomics and computational biology.
- Benefits: Remote work, competitive salary, and opportunities for professional growth.
- Why this job: Make a real impact in cancer research with cutting-edge technology.
- 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.
ML Engineer in London employer: Hlx Life Sciences
Contact Detail:
Hlx Life Sciences Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Engineer 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 biology. This will give potential employers a taste of what you can do and set 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! 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 ML Engineer 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 also genuinely excited 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 love seeing hands-on experience with omics data and deep learning, so don’t hold back on showcasing those projects that demonstrate your expertise!
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 possible to break down your skills and experiences, especially when discussing complex topics like ML pipelines.
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 machine learning fundamentals, especially in deep learning and genomics. Be ready to discuss specific projects you've worked on, particularly those involving omics data. This will show that you not only understand the theory but also have practical experience.
✨Show Your Passion for Biology
Since this role is heavily focused on computational biology, it’s crucial to express your interest in the field. Prepare to talk about any relevant experiences or insights you have regarding cancer research or translational research. This will help you connect with the interviewers on a deeper level.
✨Demonstrate Your Engineering Skills
Be prepared to discuss your experience with building production ML systems. Highlight your familiarity with version control, containerisation, and CI/CD practices. You might even want to bring examples of how you've implemented these in past projects to showcase your engineering mindset.
✨Prepare for Problem-Solving Questions
Expect to face questions that assess your ability to evaluate and improve methods based on biological insights. Think of scenarios where you had to make decisions about using ML versus simpler methods, and be ready to explain your reasoning clearly to both technical and non-technical audiences.
