AI Foundations - Research Scientist

At IBM Research, we are the innovation engine of IBM. Exploring what’s next in computing and shaping the technologies the world will rely on tomorrow. From advancing AI and hybrid cloud to pioneering practical quantum computing, we anticipate challenges and unlock new opportunities for clients, partners, and society. Working in Research means joining a team that accelerates discovery at the intersection of high-performance computing, AI, quantum, and cloud. You’ll collaborate with leading scientists, engineers, and visionaries to push boundaries and turn ideas into reality. With a culture built on curiosity, creativity, and collaboration, IBM Research offers the opportunity to grow your career while contributing to breakthroughs that transform industries and change the world. As a Research Scientist in Artificial Intelligence, you will lead cutting-edge projects in various AI and machine learning areas, creating and leveraging AI techniques to solve analytical problems using rigorous and quantitative approaches. You will also communicate your research in technical communities through publications in top-tier conferences and journals. Your primary responsibilities will include:

• 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 Tensorflow, Caffe, PyTorch, or Theano to develop and implement AI solutions.
• 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 Tensorflow, Caffe, PyTorch, or Theano 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.
• Advanced AI Framework Knowledge: Proficiency in multiple AI frameworks, including Tensorflow, Caffe, PyTorch, or Theano, with the ability to leverage these tools to develop and implement innovative AI solutions.
• Publication 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. United States Research Hybrid Professional San Jose, US San Jose (0147) International Business Machines Corporation Algorithms Artificial Intelligence Communication Computer Vision Conducting Conferences Deep Learning Integration Journals Machine Learning Natural Language Processing Publications Publishing Research Speech Caffe framework PyTorch System Integration Tensorflow Algorithm Development Properties Causal Inference Reinforcement Learning Theano Transfer Learning