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Varian Medical Systems Head of Reinforcement Learning in Palo Alto, California

Together, we can beat cancer.

At Varian, we bring together the worlds’ best talent to realize our vision of a world without fear of cancer. Together, we work passionately to develop and deliver easy-to-use, efficient oncology solutions. If you want to be part of this important mission, we want to hear from you.

Key Responsibilities

The Head of Reinforcement Learning designs, develops and programs methods, processes, and systems using Reinforcement Learning.

Because radiation plans are the product of control theory given priors, this position will lead all planning automation and human-in-the-loop as formal RL algorithms with tuning the exploitation-exploration tradeoff accordingly in each scenario:

  • Lead all DRL architectures and guide unsupervised, supervised, and self-supervised support neworks

  • Technical leadership

  • Lead and collaborate AI architecture in creation and training of RL policy engine

  • Explore and lead model-based and offline learning

  • Provide code snippets to various groups in Python

  • Technical management

  • Ensure the technical quality of the work taking place with planning and review meetings

  • Collaborate and work with other members of the Advanced AI lab

  • Work with other Varian engineers to ensure they maintain a state of the art reinforcement learning development environment

  • Documentation of all technical decisions, and why decisions were made the way they were

  • Publication of methods, as the business sees fit to disclose

  • Extract and present key findings at, and extract relevant technologies from, Machine Learning and RL conferences

  • Be able to work from Palo Alto, CA at least two days a week to collaborate with peers post COVID

Requirements

  • 10+ years of academic or professional experience in Reinforcement Learning and Control

  • Cutting edge knowledge of modern machine learning in DRL

  • Deep understanding of differential equation based RL-modeling

  • Broad background in optimization algorithms

  • Strong formal statistical skills and linear algebra skills

  • Prior, professional technical leadership in Reinforcement Learning

  • Fluent in Python and PyTorch

  • Well-published in reinforcement learning and optimization

  • Preferred:

  • Experience in healthcare industry

  • Robotics experience

#LI-OSS1

Fighting cancer calls for big ideas.

We envision a world without fear of cancer. Achieving this vision takes dedication and commitment from all of us, every single day. That's why we celebrate and value the distinctly beautiful and intersectional identities of each of our employees. We are a mirror of our patient-base, which allows us to innovate. Big ideas come from everywhere, and the best ideas are fostered by our unique individual experiences. At Varian, we encourage you to bring your whole self to work and believe your bold and authentic perspective will help to power more victories over cancer.

#TogetherWeFight

Privacy Statement at https://www.varian.com/sites/default/files/Job%20Applicant%20Privacy%20Notice%203%2027_final.pdf

Together, we can beat cancer.

Imagine a world without fear of cancer. We do, every day. Varian Medical Systems is the world’s leading manufacturer of medical devices and software for treating and managing cancer. For more than 70 years, we have developed, built, and delivered innovative cancer care technologies and solutions for our clinical partners around the globe to help them treat millions of patients each year. Taking an Intelligent Cancer Care approach, we are harnessing advanced

technologies like artificial intelligence, machine learning, and data analytics to advance cancer treatment and expand access to care to help patients become survivors.

When you join Varian, you become part of a global network of innovative and inspired minds working together across the globe. We keep the patient and our clinical partners at the center of our thinking as we power new victories in cancer care. Because for cancer patients everywhere, their fight is our fight.

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