AI Foundations - Software Engineer

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.

Your Role And Responsibilities

• Design, develop, and maintain demo systems and software prototypes that implement algorithms developed by research scientists in LLM reliability and trustworthiness
• Translate cutting-edge research (e.g., hallucination detection, uncertainty estimation, RAG pipelines, fine-tuning methods) into scalable, well-engineered software systems
• Build end-to-end pipelines integrating large language models, data processing, and evaluation frameworks
• Develop user-facing demos, APIs, or visualization tools to showcase research outcomes and system capabilities
• Collaborate closely with researchers to iterate on implementations, validate performance, and ensure fidelity between research ideas and deployed systems
• Optimize system performance, reliability, and usability for experimental and demonstration purposes

Master's Degree

Required Technical And Professional Expertise

• Strong software engineering skills in Python, with experience building production-quality or research-oriented ML systems
• Proficiency in PyTorch and familiarity with implementing and integrating deep learning models
• Understanding of large language models and Transformer-based architectures at a practical implementation level
• Experience with software system design, modular codebases, and version control (e.g., Git)
• Ability to translate research concepts into robust, maintainable code

• Experience building or deploying applications involving large language models (e.g., RAG systems, chat interfaces, evaluation pipelines)
• Familiarity with ML system infrastructure (e.g., APIs, distributed systems, cloud platforms)
• Experience developing interactive demos, dashboards, or visualization tools
• Prior collaboration with research teams or experience implementing research prototypes
• Exposure to topics such as uncertainty estimation, hallucination detection, or LLM evaluation frameworks