ML Engineering Director, AI for Drug Discovery

The Position A healthier future. It's what drives us to innovate. At Roche and Genentech we continuously advance science to ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That's what makes us Roche. Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche's Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Data sharing and access to models across our large R&D system are essential to maximising these opportunities. The new Computational Sciences Center of Excellence (CS CoE) is a strategic, unified group whose goal is to harness the transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide. The Opportunity: We are seeking an experienced Head of Engineering, reporting to the VP of Artificial Intelligence for Drug Discovery (AI4DD), to lead the technical development, product engineering and infrastructure for our AI for Drug Discovery (AI4DD) group. This role is central to accelerating our mission of leveraging cutting-edge machine learning and AI to transform pharmaceutical research and development. The Head of Engineering oversees all facets of AI4DD engineering including deploying high impact production applications, data engineering pipelines, and machine learning infrastructure. This is a leadership position requiring deep technical expertise in AI/ML engineering, large-scale data systems, and a passion for applying technology to complex R&D challenges. In this role, you will: Technical Leadership and Strategy Own technical strategy and roadmap for the AI4DD engineering function, ensuring alignment with the overall research goals and pharmaceutical portfolio needs. Lead the ML Engineering and Infrastructure team designing, and maintaining a robust, scalable, and secure ML platforms to support machine learning model training, experimentation, deployment, and inference for frontier research. Align closely with other related CSCoE departments across the whole system and data value chain. Partner closely with our LLM and Agent research teams to optimize infrastructure for foundation model training and the orchestration of autonomous agentic workflows. Partner closely with our Large and Small Molecule Portfolio teams to ensure data infrastructure, resource, and product alignment for maximum acceleration of the drug discovery pipeline Oversee the Data Engineering team's efforts in the architecture and development of data pipelines and data products, ensuring high-quality, traceable, and accessible data for advanced early phase R&D activities. Direct the Product Engineering team's development of researcher-facing applications and tools, such as protein design interfaces and predictive modeling platforms. Team Management and Development Lead, mentor, and grow a diverse team of machine learning, software, and data engineers as well as developers. Foster a culture of technical excellence, continuous integration, robust testing, and collaborative problem-solving. Manage project execution, resource allocation, and budget planning for all engineering initiatives. Cross-Functional Collaboration Work closely with AI/ML Research Scientists, Computational Biologists, and therapeutic areas leads to translate research prototypes into production-ready systems and tools. Collaborate with IT and security teams to ensure engineering practices adhere to all necessary enterprise standards. Who you are: At least 5 years of experience in software, data or ML engineering, with at least 5 years in a people leadership role managing multi-disciplinary engineering teams. Experience in designing scalable, reliable, and cost-effective data products and computational systems and extensive hands-on experience building, scaling, and maintaining infrastructure specifically for machine learning/AI workflows (MLOps, distributed training, inference services, batch compute, containerization, infrastructure-as-code). Deep expertise in cloud computing environments (e.g., AWS, GCP, Azure), modern data stack technologies, and languages and frameworks commonly used in ML (e.g., Python, PyTorch). Deep understanding of machine learning algorithms, model evaluation techniques, and the infrastructure and lifecycle of developing AI models for complex, predictive domains including LLMs (distributed fine-tuning, and high-performance inference.) Preferred Experience in the biotech or pharmaceutical industry, ideally with exposure to drug discovery, preclinical development, or clinical trial data. A strong understanding of computational biology, cheminformatics, or molecular simulation is highly desirable. Master's degree in Computer Science, Engineering or a related quantitative field. Experience managing multi-site teams. Relocation benefits are available for this opportunity The expected salary range for this position, based on the primary location of New York, is $192,400 - $357,400 of hiring range. For the primary of location of California, $201,200 - $373,600 of hiring range. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below. Benefits #ComputationCoE #tech4lifeComputationalScience #tech4lifeAI Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws. 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