Machine Learning Engineer / Drug Discovery / £100k + Equity

AI is transforming drug discovery... but there’s a problem.

Most models are built on sparse, fragmented, and low-quality data.

So instead of accelerating breakthroughs, they often lead to dead ends.

We’re working with a cutting-edge, seed-stage start-up building an AI-native platform powered by deeply curated, high-quality experimental molecular data, unlocking better predictions across potency, binding, and ADMET.

Their platform is already used by hundreds of chemists globally, directly impacting real-world programs across oncology, neurodegeneration, inflammation, and global health.

Now, they’re hiring a Founding Machine Learning Engineer to help define the future of AI-driven drug design.

⭐️What you’ll be doing⭐️

Building state-of-the-art models for molecular property prediction, including foundation models and AutoML pipelines

Designing and scaling ML infrastructure (training pipelines, experiment tracking, model registry, CI/CD)

Deploying low-latency, production-grade model serving systems

Developing robust data pipelines for dataset curation, validation, and versioning

Working closely with scientists, product teams, and users to ship impactful features

⭐️What we’re looking for⭐️

3+ years building and deploying ML systems in production (not just research)

Strong software engineering fundamentals

Experience with MLOps tooling, model serving, and containerisation

Comfortable working with cloud infrastructure (AWS, GCP, or Azure)

High ownership mindset with the ability to operate in ambiguity

⭐️Nice to have⭐️

Background in computational chemistry, physics, or related fields

Contributions to open-source ML or scientific tooling

Experience deploying ML systems at scale

If this sounds interesting, even if you do not meet all of the requirements, please apply with your CV attached.