We are supporting a deeptech organisation dedicated to transforming the way new therapies are discovered and developed. By applying cutting-edge artificial intelligence to complex biological data, this company is accelerating the validation of therapeutic targets and enabling the development of more personalised treatments.

Having recently secured significant funding, the organisation is entering an exciting growth phase and is seeking top talent to join their mission-driven team.

This role will offer you:

• The opportunity to design and optimise state-of-the-art ML models applied to diverse biomedical datasets.

• A central role in building scalable ML infrastructure and production-ready pipelines.

• Collaboration with an international, multidisciplinary team of AI researchers, computational biologists, and domain experts.

• A chance to contribute to impactful science and technology with real-world applications in healthcare.

Responsibilities:

• Develop, implement, and fine-tune ML models for large-scale, multimodal biomedical data.

• Build and maintain ML pipelines, including preprocessing, training, evaluation, and deployment.

• Apply best practices in MLOps for versioning, monitoring, and reproducibility.

• Transform raw datasets into deep-learning-ready data loaders that can scale to millions of datapoints.

• Partner closely with scientific and technical experts to ensure models align with real-world research challenges.

You will bring:

• Master’s degree or PhD in Machine Learning, Computer Science, Data Science, or a related field.

• 3+ years of hands-on experience with deep learning frameworks (PyTorch, TensorFlow, JAX).

• Strong Python skills and experience with production ML systems (Docker, Kubernetes, cloud platforms such as AWS/GCP/Azure).

• Familiarity with MLOps tools (Kubeflow, MLflow, Weights & Biases, Metaflow) and DevOps practices.

• Experience with distributed training, GPU optimisation, and large-scale model development.

How to stand out:

• Background in biological, clinical, or healthcare data.

• Experience with foundation models or multimodal data.

• A track record of thriving in fast-paced, dynamic environments.

• A collaborative mindset with strong problem-solving skills.