Machine Learning Manager, London (Hiring Immediately)

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.

About Iso

Isomorphic Labs (IsoLabs) was launched in 2021 to advance human health by building on and beyond the Nobel-winning AlphaFold 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 technologies and methodologies to enhance our capabilities and solve challenging problems in drug discovery.
• Project Management & Execution: Oversee the execution of complex ML engineering projects, ensuring timely delivery and alignment with organizational goals. Manage priorities, resources, and timelines effectively.
• Operational Excellence: Ensure the reliability, scalability, and efficiency of our ML systems in a production environment. Implement robust monitoring, alerting, and incident response processes.

Skills and qualifications

Essential:

• Demonstrable experience in an ML engineering leadership or management role, including mentoring and guiding engineering teams.
• Proven experience in software engineering with a significant focus on machine learning.
• Strong proficiency in Python and experience with common machine learning libraries and frameworks (e.g., TensorFlow, PyTorch, JAX, scikit-learn).
• Solid understanding of machine learning concepts, algorithms, and best practices (e.g., deep learning, reinforcement learning, generative models, MLOps).
• Experience in designing, building, and deploying scalable ML systems in production environments (e.g., on cloud platforms like GCP, AWS, or Azure).
• Excellent software engineering fundamentals, including data structures, algorithms, software design patterns, and distributed systems.
• Experience with MLOps tools and practices (e.g., Kubeflow, MLflow, Airflow, CI/CD for ML).
• Strong communication, collaboration, and problem-solving skills.
• Ability to thrive in a fast-paced, innovative, and interdisciplinary research environment.
• MSc or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience.

Preferred Qualifications:

• Experience working in a scientific research environment, particularly in drug discovery, bioinformatics, cheminformatics, or computational biology.
• Familiarity with large-scale data processing frameworks (e.g., Apache Spark, Beam).
• Experience with containerization technologies (e.g., Docker, Kubernetes).
• Contributions to open-source ML projects.
• Track record of leading impactful ML projects from conception to deployment.
• Experience working with very large datasets.

Culture and values

We are guided by our shared values. It’s not about finding people who think and act in the same way. These values help to guide our work and will continue to strengthen it.

• Thoughtful: Thoughtful at Iso is about curiosity, creativity and care. It is about good people doing good, rigorous and future-making science every single day.
• Brave: Brave at Iso is about fearlessness, but it’s also about initiative and integrity. The scale of the challenge demands nothing less.
• Determined: Determined at Iso is the way we pursue our goal. It’s a confidence in our hypothesis, as well as the urgency and agility needed to deliver on it. Because disease won’t wait, so neither should we.
• Together: Together at Iso is about connection, collaboration across fields and catalytic relationships. It’s knowing that transformation is a group project, and remembering that what we’re doing will have a real impact on real people everywhere.

Creating an extraordinary company

We believe that to be successful we need a team with a range of skills and talents. We’re building an environment where collaboration is fundamental, learning is shared and every employee feels supported and able to thrive. We value unique experiences, knowledge, backgrounds, and perspectives, and harness these qualities to create extraordinary impact.

We are committed to equal employment opportunities regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy or related condition (including breastfeeding) or any other basis protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know.

Hybrid working

It’s hugely important for us to share knowledge and build strong relationships with each other, and we find it easier to do this if we spend time together in person. This is why we follow a hybrid model, and would require you to be able to come into the office 3 days a week (currently Tuesday, Wednesday, and one other day depending on which team you’re in). If you have additional needs that would prevent you from following this hybrid approach, we’d be happy to talk through these if you’re selected for.