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Principal Machine Learning Scientist

3 months ago

San Francisco, United States Genentech Full time
The Position

A healthier future. It’s what drives us to innovate. To continuously advance science and 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 Genentech.

The Opportunity

We are seeking a highly skilled and motivated Principal Machine Learning Scientist to join the BRAID team (Biology Research | AI Development) within our Computational Sciences organization. This role will focus on building novel machine-learning methods to enhance drug development and clinical trial design, with a particular emphasis on early-stage clinical trials. The successful candidate will develop novel machine learning methods for multimodal clinical data, including generative models and representation learning, leveraging their expertise to improve trial efficiency, patient outcomes, and overall trial success rates. The candidate is expected to routinely publish work in top-tier Machine Learning and scientific venues.

We seek exceptional researchers with a demonstrated research background in machine learning, a passion for interdisciplinary research and technical problem-solving, and a proven ability to develop and implement ideas from research.

Key Responsibilities

  • Lead research initiatives at the intersection of machine learning, biology, and clinical sciences.
  • Conceive and lead collaborations both internally and externally.
  • Maintain expertise at the forefront of multiple areas in machine learning and biology/clinical sciences.
  • Collaborate with cross-functional teams, including chemists, biostatisticians, clinical scientists, and data engineers, to integrate ML methods into clinical trial development.
  • Lead and contribute to the design and execution of clinical trials by developing foundation models using multimodal pre-clinical and clinical datasets.
  • Publish research findings in top-tier machine learning and scientific journals and present at leading conferences.
  • Mentor junior scientists and contribute to the strategic direction of the ML group within the Computational Science Organization.
Who you are
  • Educational Background: Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, Physics, or a related field with a strong emphasis on machine learning.
  • Experience: Proven track record with 2+ years of experience developing and applying advanced ML models in a research or industry setting.
  • Technical Skills:
    • Proficiency in scientific programming languages such as Python as well as MLOps workflows (e.g., familiar with code version control, high-performance compute infrastructures, and machine learning experiment monitoring workflows).
    • Extensive experience with machine learning frameworks and libraries (e.g., JAX, PyTorch, Tensorflow).
    • Strong background in statistics, probabilistic modeling, and data analysis.
  • Domain Knowledge: Understanding of clinical trials, drug development, and biological data.
  • Soft Skills: Excellent communication, collaboration, and problem-solving skills.
  • Publications: Strong publication record and experience contributing to research communities, including scientific journals and conferences like NeurIPS, ICML, ICLR, CVPR, ICCV, etc.

Preferred

  • You have an extensive track record of delivering innovative solutions in machine learning, strong communication skills with the ability to effectively communicate technical concepts to both technical and non-technical audiences as well as interfacing with scientific leadership.
  • You have practical experience in one or more of the following areas: reinforcement learning, geometric deep learning, representation learning, and generative learning.
  • You have a passion for solving complex technical problems and a commitment to staying up-to-date with the latest developments in machine learning.
  • You have experience in early-stage clinical trial design and execution and knowledge of regulatory requirements and standards in clinical trials.
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