Machine Learning Manager

Machine Learning Engineering Lead, London 

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.