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.