Principal Research Scientist

Introduction

IBM Research Scientists are charting the future of Artificial Intelligence, creating breakthroughs in quantum computing, discovering how blockchain will reshape the enterprise, and much more. Join a team that is dedicated to applying science to some of today's most complex challenges, whether it's discovering a new way for doctors to help patients, teaming with environmentalists to clean up our waterways or enabling retailers to personalize customer service.

Your Role and Responsibilities

IBM Research is seeking a highly skilled and motivated AI Scientist/Engineer with expertise in PyTorch to join our team. In this role, you will be responsible for developing, training, and deploying sophisticated machine learning models. You will collaborate with a cross-functional team to translate business requirements into robust AI solutions, ensuring high performance and scalability.

Roles and Responsibilities:

• Develop, train, and optimize machine learning models using PyTorch.

• Collaborate with cross-disciplinary teams to understand requirements and formulate AI-driven solutions.

• Implement state-of-the-art algorithms and contribute to the development of our AI platform.

• Evaluate and improve the performance of machine learning models.

• Stay abreast of latest developments in AI and machine learning, especially in PyTorch.

• Prepare and present findings to both technical and non-technical stakeholders.

• Contribute to the codebase ensuring best practices in software development.

Required Technical and Professional Expertise

• PhD in Computer Science, Machine Learning, Artificial Intelligence, or related field.

• Proficient in Python and PyTorch with a minimum of 3 years of hands-on experience.

• Strong understanding of machine learning algorithms, deep learning architectures, and optimization techniques.

• Excellent problem-solving skills and the ability to think critically and analytically.

• Strong communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.

Preferred Technical and Professional Expertise

• Experience with other deep learning frameworks (e.g., TensorFlow, Keras).

• Familiarity with cloud platforms (e.g., AWS, GCP, Azure) and their machine learning services.

• Experience with big data technologies (e.g., Hadoop, Spark).

• Contributions to open-source machine learning projects.