Software Engineer (Machine Learning Research Engineering)

Isomorphic Labs is a new Alphabet company that is reimagining drug discovery through a computational- and AI-first approach. We are on a mission to accelerate the speed, increase the efficacy and lower the cost of drug discovery. You'll be working at the cutting edge of the new era of 'digital biology' to deliver a transformative social impact for the benefit of millions of people.

This is an exciting opportunity for you to work on a greenfield ML-based software platform that will transform the biopharmaceutical world as we know it. Working in a highly creative, iterative environment, you will be partnering with leading engineers, scientists and ML researchers to build the critical platform driving that transformation.

What you will do:

• Partner with the ML Research, Drug Design, and Data Engineering teams to design, develop, train, and evaluate a variety of cutting edge ML models at unprecedented scale.
• Build a world-class ML drug design research environment with scalable software and libraries.
• Develop tools and libraries to enable large-scale machine learning experiments across thousands of accelerators.
• Maximise model performance, scalability, and robustness for production use within our computational platform.
• Create novel instrumental drug discovery tools in partnership with domain experts. Take end-to-end ownership from rapid prototyping to production-quality code.
• Iterate collaboratively with scientists and domain experts to deeply understand feature requirements and user feedback.

Culture and values:

• Thoughtful: Curiosity, creativity, and care. Good people doing good, rigorous and future-making science every single day.
• Brave: Fearlessness, initiative, and integrity.
• Determined: Confidence in our hypothesis, urgency, and agility needed to deliver on it.
• Together: Connection, collaboration across fields, and catalytic relationships.

We are committed to equal employment opportunities and building supportive and inclusive teams that reflect and represent the populations we are striving to serve.