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- Tasks: Lead the development of ML methods for cancer research and drug development.
- 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 healthcare by transforming complex data into actionable insights.
- 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 48000 - 72000 £ 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 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 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 biological concepts. Be ready to discuss your past experiences and how they relate to the role. Practice explaining complex ideas simply; it’ll impress both techies and non-techies alike!
✨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 genuinely interested in joining our mission to advance cancer research.
We think you need these skills to ace Senior 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 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 tackled challenges and what impact your work had.
Demonstrate Your Technical Skills: Don’t hold back on showcasing your Python prowess and any experience with deep learning models. Mention any tools or practices you’re familiar with, like Git, Docker, or CI/CD, as these are crucial for our production-quality standards.
Tailor Your Application: Take a moment to customise your application for this role. We appreciate when candidates take the time to align their skills and experiences with what we’re looking for. Apply through our website to make sure your application gets to us directly!
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 how you've applied deep learning techniques in real-world scenarios, especially in relation to omics data. This role is all about turning complex data into actionable insights, so show them you can do just that!
✨Showcase Your Projects
Prepare to talk about specific projects where you've designed and implemented ML pipelines. Highlight your experience with tools like Python, Docker, and Git. If you have examples of how you've improved methods based on feedback or failures, share those stories—they'll want to see your problem-solving skills in action.
✨Understand the Business Impact
This isn't just about tech; it's about making a difference in cancer research and drug development. Be ready to explain how your work can add value to the company’s goals. Discuss how you've evaluated emerging methods and made decisions based on evidence rather than trends—this will show you're aligned with their mission.
✨Communicate Clearly
You'll need to explain complex concepts to both technical and non-technical stakeholders. Practice articulating your thoughts on trade-offs in ML methods and be prepared for questions that test your ability to simplify complex ideas. Good communication can set you apart from other candidates!
