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Principal Machine Learning Engineer

Sajawo
London
1 week ago
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Why this role matters

We’re on a mission to change the way science meets technology. Building AI that accelerates discovery, improves lives, and shapes the future of biotech. As our Principal Machine Learning Engineer, you’ll be at the heart of that mission: bringing advanced ML models into real-world use, guiding technical strategy, and mentoring a growing team.

This isn’t just about writing code, it’s about solving problems that have never been solved before, alongside people who share your curiosity and drive.

What you’ll be doing

  • Designing and leading the development of scalable ML models for biological and biomedical data.

  • Driving technical direction across ML projects — from prototyping through to deployment.

  • Collaborating closely with biologists, data scientists, and software engineers to translate science into solutions.

  • Mentoring and supporting engineers, helping to build a culture of learning and innovation.

  • Staying hands-on with the codebase — ensuring models are robust, explainable, and production-ready.

  • Evaluating and introducing new tools, frameworks, and approaches to keep us ahead of the curve.



RequirementsWhat we’re looking for

  • A strong background in machine learning and deep learning, ideally with applications in life sciences, healthcare, or related fields.

  • Solid experience in Python and ML frameworks such as PyTorch or TensorFlow.

  • A track record of taking ML models from concept to production.

  • Comfort working with large, complex, and sometimes messy datasets.

  • An interest in working across disciplines, you don’t need a biology degree but you should be curious to learn.

  • Leadership experience: you’ve guided projects, supported teammates, and influenced technical direction.


Nice-to-haves

  • Experience with multi-modal data (omics, imaging, clinical data).

  • Knowledge of MLOps practices and deployment at scale.

  • Contributions to open-source ML/AI projects.



Benefits

Competitive salary & bonus: rewarding the impact you make.

Flexible working: hybrid options to suit your life, not just your work.

Learning & growth: budget for courses, conferences, and personal development.

Collaborative culture: open, supportive, and built on curiosity.

Mission with meaning: work on problems that truly matter in AI and biotech.



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