Research Scientist, Machine Learning Fundamentals

vor 1 Monat

CH Zürich Google Switzerland GmbH Vollzeit

As an organization, Google maintains a portfolio of research projects driven by fundamental research, new product innovation, product contribution and infrastructure goals, while providing individuals and teams the freedom to emphasize specific types of work. As a Research Scientist, you'll setup large-scale tests and deploy promising ideas quickly and broadly, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies. From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more.

As a Research Scientist, you'll also actively contribute to the wider research community by sharing and publishing your findings, with ideas inspired by internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.

Google Research addresses challenges that define the technology of today and tomorrow. From conducting fundamental research to influencing product development, our research teams have the opportunity to impact technology used by billions of people every day.

Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field -- we publish regularly in academic journals, release projects as open source, and apply research to Google products.

Minimum qualifications:
  • PhD degree in Computer Science, a related field, or equivalent practical experience.
  • Experience writing machine learning code, prototyping with machine learning models, and working with transformer as well as transformer alternatives like SSMs and modern RNNs models.
  • One or more scientific publication submission(s) for conferences, journals, or public repositories.
  • Publications record in neural networks, machine learning, neural network accelerator, neuromorphic hardware or (computational) neuroscience.
Preferred qualifications:
  • Experience writing ML code, serving large ML models, and serving large-scale models.
  • Experience in writing high profile scientific publications at conference (NeurIPS, Nature).
  • Experience in neural network theory, neuromorphic hardware, and/or computational neuroscience.
  • Experience in hardware aware design of neural networks/neuromorphic hardware.
  • Advance neural network models from the bottom up.
  • Help invent novel scalable ML architectures that exceed on alternative hardware.
  • Write machine learning code (e.g., JAX, CUDA, C++).
  • Work with external and internal partners to advance ML algorithms.
  • Work on computational neuroscience and neural networks theory.

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