AI Foundations - Research Scientist

IBM

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Work type
On-site
Location
Cambridge, MA
Posted
10 days ago
Nearby
99+ roles within 25 mi

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Listed pay typically runs $1,000,000–$1,000,000 across 9 roles with salary data.

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TL;DR · AI Foundations - Research Scientist

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As a Research Scientist in Artificial Intelligence at the AI Foundations team, you will lead advanced projects in deep learning, reinforcement learning, and other cutting-edge areas, developing innovative AI techniques to solve complex analytical problems. Your day-to-day responsibilities include proving mathematical properties of algorithms through rigorous experiments, integrating research into practical systems, and publishing findings in top-tier conferences like NIPS and ICML. You must be proficient in frameworks such as PyTorch and TensorFlow, possess deep expertise in multiple AI/ML domains including computer vision and natural language processing, and have a track record of successful algorithm development and system integration. This role demands an interdisciplinary approach to tackle large-scale business challenges with novel solutions.

Skills

PyTorch Tensorflow deep learning reinforcement learning lifelong learning transfer learning few-shot learning interpretable learning adversarial learning learning with memories KRR symbolic logic trainable logic causal inference computer vision speech NLP brain-inspired algorithms neuromorphic architectures CI/CD

What you'll do

  • Lead projects in deep learning, reinforcement learning, and lifelong learning to solve complex analytical problems.
  • Develop and prove mathematical properties of AI algorithms through rigorous experiments.
  • Embed research algorithms into usable systems for effective application.
  • Publish research findings in top-tier conferences like NIPS and ICML.
  • Utilize frameworks such as PyTorch and TensorFlow to implement AI solutions.
  • Communicate complex research to technical audiences through publications.

What we're looking for

  • Deep expertise in multiple AI/ML areas including deep learning, reinforcement learning, and computer vision.
  • Experience developing and proving mathematical properties of algorithms through rigorous experiments.
  • Proficiency in embedding research algorithms into usable systems for practical application.
  • Strong publication record in top-tier AI conferences like NIPS, CVPR, ICML, and ICLR.
  • Advanced knowledge and proficiency with AI frameworks such as PyTorch and TensorFlow.
  • Ability to communicate complex research findings effectively to technical audiences through publications.

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