FAQs
What is the primary focus of this role as a Principal Machine Learning Engineer, Infrastructure (LLM)?
The primary focus of this role is to design, construct, and optimize large-scale distributed systems, with a particular emphasis on machine learning infrastructure, specifically applicable to drug discovery and large language models (LLMs).
What qualifications are required for this position?
The qualifications required for this position include a MS or PhD in Computer Science, Statistics, Machine learning or related field, 6+ years of industry experience relating to distributed systems and machine learning, proven expertise in designing and implementing large-scale machine learning models and infrastructure, technical leadership experience, hands-on experience with PyTorch, excellent programming skills, familiarity with large language models (LLMs), and a keen interest in molecular design.
What will the responsibilities of this role entail?
The responsibilities of this role include contributing to cutting-edge research in machine learning, developing and maintaining tools for large-scale ML experiments, tackling engineering challenges related to dynamic, distributed, and high-performance machine learning systems, and collaborating with cross-functional teams to solve complex problems in the life sciences.
What kind of projects or research will I be working on in this role?
You will be working on state-of-the-art systems, contributing to cutting-edge research, developing innovative algorithms, and playing an essential part in Prescient Design's success. Projects will involve the development of large language models and AI systems used for scientific discovery.
What kind of team will I be working with in this role?
You will be working with a dynamic and challenging team in Genentech Computational Sciences (gCS) Prescient Design, consisting of researchers, engineers, and scientists, as well as close links to top academic institutions around the world and internal Genentech Research and Early Development (gRED) partners and research units.