Overview
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 contribute towards achieving our ambitious goals within 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 Manager 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.
Responsibilities
- Technical Leadership and Vision: Provide technical direction and leadership for a team of ML, full-stack 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 and Development: Mentor and grow teams of ML software engineers, fostering a culture of technical excellence, innovation, and collaboration. Provide guidance on career development, best practices, and problem-solving.
- ML System Design and 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 and 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 and 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.
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
- Thoughtful: Curiosity, creativity and care; rigorous, future-making science.
- Brave: Initiative and integrity; tackling large-scale challenges.
- Determined: Confidence in our hypothesis, urgency, and agility to deliver.
- Together: Collaboration across fields and catalytic relationships; transformation is a team effort.
- Creating An Extraordinary Company: We value diverse skills and backgrounds and foster an environment where everyone can thrive. We believe in equal opportunity and inclusivity.
Working arrangements
Hybrid working: We follow a hybrid model and would require you to be able to come into the office 3 days a week (Tuesday, Wednesday, and one other day depending on your team). If you have additional needs that would prevent you from following this hybrid approach, we’re happy to discuss accommodations.
Equal opportunity and privacy
We are committed to equal employment opportunities regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital or 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.
Please note that when you submit an application, your data will be processed in line with our privacy policy.
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