AI Foundations - Research Scientist

Role Overview

At IBM Research, we are dedicated to driving innovation and shaping the future of technology. Our team collaborates with leading scientists, engineers, and visionaries to advance fields such as AI, hybrid cloud, and quantum computing. The Research Scientist in Artificial Intelligence position involves leading cutting-edge projects in AI and machine learning, developing and applying AI techniques to solve complex analytical problems, and communicating research findings through publications in top-tier conferences and journals.

Responsibilities

• Lead AI Projects: Lead projects in deep learning, reinforcement learning, lifelong learning, and other AI/ML areas, creating and applying AI techniques to solve complex analytical problems.
• Develop and Prove Algorithms: Develop and prove mathematical properties of algorithms, conducting successful experiments to demonstrate these properties, and evaluating their impact on user experiences.
• Embed Research in Systems: Embed research algorithms in usable systems, ensuring seamless integration and effective application of AI techniques.
• Communicate Research: Communicate research findings through publications in top-tier conferences and journals, such as NIPS, CVPR, ICML, and ICLR.
• Leverage AI Frameworks: Utilize frameworks like PyTorch, TensorFlow to develop and implement AI solutions.

Requirements

• Doctorate Degree Required
• Deep Expertise in AI/ML: Deep expertise in one or more AI/ML areas, including deep learning, reinforcement learning, lifelong learning, transfer learning, few-shot learning, interpretable and adversarial learning, learning with memories, KRR, symbolic and trainable logic, causal inference, computer vision, speech, NLP, brain-inspired algorithms, and neuromorphic architectures.
• Proven Algorithm Development: Experience with developing and proving mathematical properties of algorithms, conducting successful experiments to demonstrate these properties, and evaluating their impact on user experiences.
• AI Framework Proficiency: Proficiency in utilizing frameworks like PyTorch, TensorFlow to develop and implement AI solutions.
• Research Communication: Experience with communicating research findings through publications in top-tier conferences and journals, such as NIPS, CVPR, ICML, and ICLR.
• System Integration: Experience with embedding research algorithms in usable systems, ensuring seamless integration and effective application of AI techniques.

Preferred Education and Experience

• Advanced AI Framework Knowledge: Proficiency in multiple AI frameworks, including PyTorch, TensorFlow, with the ability to leverage these tools to develop and implement innovative AI solutions.
• Publishation in Top-Tier Journals: Experience publishing research findings in prestigious conferences and journals beyond the required top-tier outlets, such as NIPS, CVPR, ICML, and ICLR, showcasing expertise in communicating complex research to technical audiences.
• Interdisciplinary AI Expertise: Deep expertise in multiple AI/ML areas, enabling the development of novel AI techniques and solutions that integrate concepts from diverse fields, such as computer vision, speech, NLP, and brain-inspired algorithms.