Principal Machine Learning Engineer, Infrastructure

2 weeks ago


New York, New York, United States Genentech Full time
About the Role

Genentech is seeking a Principal Machine Learning Engineer to join our team in New York. As a key member of our engineering team, you will be responsible for designing, constructing, and optimizing large-scale distributed systems, with a particular emphasis on machine learning infrastructure.

You will work on state-of-the-art systems, contribute to cutting-edge research, develop innovative algorithms, and play an essential part in Prescient Design's success.

The group provides a dynamic and challenging environment for cutting-edge, multidisciplinary research, including access to heterogeneous data sources, close links to top academic institutions around the world, and internal Genentech Research and Early Development (gRED) partners and research units.

Key Responsibilities
  • Contribute to cutting-edge research in machine learning, including algorithm and method development, specifically applicable to drug discovery and large language models (LLMs).
  • Develop and maintain robust tools for large-scale ML experiments, focusing on the deployment, optimization, and scalability of compute-intensive LLM training and lab-in-the-loop molecular design.
  • Tackle crucial engineering challenges that involve the design, implementation, and scaling of dynamic, distributed, and high-performance machine learning systems such as models deployed as APIs, retrieval augmented generation (RAG) systems, search and advanced generative models of text, images, and structures, such as molecules.
  • Engage in close collaboration with cross-functional teams across both Prescient Design, gCS, gRED, and global Roche to solve complex problems in the life sciences, including leveraging language models for scientific applications.
Requirements
  • MS or PhD in Computer Science, Statistics, Machine learning or related field, or equivalent experience.
  • 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.
  • Demonstrated success in technical leadership for teams of engineers.
  • Practical hands-on experience with PyTorch in a production environment.
  • Excellent programming skills.
  • Familiarity with large language models (LLMs) and their training is highly desirable.


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