Machine Learning Manager

Isomorphic Labs is applying frontier AI to help unlock deeper scientific insights, faster breakthroughs, and life-changing medicines with an ambition to solve all disease.

The future is coming. A future enabled and enriched by the incredible power of machine learning. A future in which diseases are curtailed or cured starting with better and faster drug discovery.

Come and be part of an interdisciplinary team driving groundbreaking innovation and play a meaningful role in contributing towards us achieving our ambitious goals, while being a part of an inspiring and collaborative culture.

The world we want tomorrow is the one we’re building today. It starts with the culture at this company. It starts with you.

Isomorphic Labs (Iso Labs) was launched in 2021 to advance human health by building on and beyond the Nobel-winning Alpha Fold system. Since then, our interdisciplinary team of drug discovery experts and machine learning specialists has built powerful new predictive and generative AI models that accelerate scientific discovery at digital speed.

Our name comes from the belief that there is an underlying symmetry between biology and information science. By harnessing AI’s powerful capabilities, we can use it to model complex biological phenomena to help design novel molecules, anticipate how drugs will perform and develop innovative medicines to treat and cure some of the world’s most devastating diseases.

We have built a world-leading drug design engine comprising AI models that are capable of working across multiple therapeutic areas and drug modalities. We are continually innovating on model architecture and developing cutting-edge capabilities to advance rational drug design.

Every day, and with each new breakthrough, we’re getting closer to the promise of digital biology, and achieving our ambitious mission to one day solve all disease with the help of AI.

Your Impact

As a Machine Learning Software Engineer Lead at Isomorphic Labs, you will play a pivotal role in shaping and driving the engineering foundations that underpin our AI-first approach to drug discovery. You will lead a talented team of ML and full stack software engineers, guiding them in building robust, scalable, and innovative machine learning systems and infrastructure. Your work will directly contribute to translating groundbreaking research into tangible tools and platforms that accelerate the discovery of new medicines.

This is a unique opportunity to combine your passion for machine learning, software engineering excellence, and leadership to make a significant impact on human health.

Key Responsibilities:

• Technical Leadership & Vision: Provide technical direction and leadership for a team of ML, Fullstack and Backend Software Engineers. Define and drive the technical roadmap for ML systems, infrastructure, and tooling in collaboration with research scientists, ML researchers, and other engineering teams.
• Team Mentorship & Development: Mentor and grow teams of ML SWEs, Fullstack and Backend SWEs, fostering a culture of technical excellence, innovation, and collaboration. Provide guidance on career development, best practices, and problem-solving.
• ML System Design & Implementation: Lead the design, development, deployment, and maintenance of scalable and production-ready machine learning models, pipelines, and platforms. This includes data ingestion, preprocessing, model training, evaluation, serving, and monitoring.
• Software Engineering Excellence: Champion best practices in software engineering, including code quality, testing, CI/CD, version control, documentation, and infrastructure as code. Ensure the team delivers high-quality, maintainable, and efficient software.
• Cross-Functional Collaboration: Work closely with AI researchers, biologists, chemists, and other engineers to understand their needs, translate research ideas into production systems, and ensure the successful application of ML to complex scientific challenges.
• Innovation & Problem Solving: Stay at the forefront of advancements in machine learning, MLOps, and software engineering. Identify and evaluate new…