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.
IBM Research is seeking an innovative and driven Research Engineer to push the boundaries of Artificial Intelligence. In this role, you will be at the forefront of natural language processing (NLP), deep learning, and foundation model development. You will bridge the gap between cutting-edge theoretical research and practical application by building, training, and analyzing large language models (LLMs). This position offers a unique opportunity to shape the future of AI interpretability, collaborate with world-class scientists and academic partners, and publish groundbreaking findings at top-tier industry venues. If you are passionate about rapidly prototyping AI-driven solutions and scaling complex data pipelines, we invite you to join our team.
- Deep expertise in natural language processing (NLP), deep learning, and foundation model development.
- Hands-on experience designing, training, and working with Transformer architectures and Large Language Models (LLMs).
- Strong programming proficiency in Python and deep learning frameworks, specifically PyTorch.
- Demonstrated ability to rapidly prototype, build, and iterate on AI-driven solutions.
- Experience with or a strong understanding of mechanistic interpretability for AI models.
- Proficiency in systems programming languages, particularly Rust or C++.
- Proven experience designing, building, and managing large-scale data pipelines.
- Background in developing interactive analytical or visualization tools for comprehensive model analysis.