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Principal Machine Learning Engineer, Infrastructure (LLM)

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Genentech

19d ago

  • Job
    Full-time
    Senior Level
  • Data
  • New York City, +1

AI generated summary

  • You need a MS/PhD, 6+ years in distributed systems & machine learning, large-scale model expertise, leadership experience, PyTorch skills, programming skills, LLM familiarity, & interest in molecular design.
  • You will lead machine learning research, develop tools for large-scale ML experiments, and engineer high-performance systems for drug discovery and LLM applications in collaboration with cross-functional teams.

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.
  • A keen interest in molecular design, underpinned by a desire to contribute to both scientific and computational advancements in the field.

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.

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.

Science & Healthcare
Industry
10,001+
Employees
1976
Founded Year

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