Intern

About the Role

IBM Research and IBM Software Innovation Lab India offer Summer Internship programs to bright students from leading technical universities. These programs provide industry experience and exposure to world-class technologies, allowing students to showcase their talent and work on current projects with mentors. The goal is to offer students industry experience and identify future employees.

Key Responsibilities

• Contribute to research and development efforts across multiple frontiers of AI.
• Work on innovation projects grounded in real-world datasets, environments, and problems.
• Push the state-of-the-art in agentic AI technology, with a specific focus on enterprise applications.
• Work on one or more research areas, including:
• Building and scaling large language models.
• Developing agentic systems that work with small language models.
• Auto-evolution of agents, including testing and debugging.
• Software stack for AI Accelerator cards.
• Intersection of data and AI.
• Programmable language models.
• Collaborate with partners to produce state-of-the-art demonstrations on IBM Quantum hardware.
• Read, understand, and critically analyze research papers.
• Reproduce research results and design controlled experiments.

Research Areas Include

• AI for IT automation
• Intersection of data and AI
• Agentic middleware and models
• Embedding and AI-based retrieval
• Time series foundation models
• Multi-modal foundation models
• AI for software engineering
• Building and scaling large language models
• Agentic systems
• Auto-evolution of agents
• Software stack for AI Accelerator cards
• Programmable language models

Requirements & Tech Stack

• Python
• Pandas
• NumPy
• Data Structures
• Algorithms
• Problem Solving
• Linear Algebra
• Probability
• Statistics
• VS Code
• Jupyter Notebooks
• Git
• AI/ML Fundamentals (supervised & unsupervised learning, classification, regression, neural networks, clustering)
• Learning fundamentals and Data Manipulation/Analysis for large datasets
• TensorFlow
• PyTorch
• Deep Learning
• Foundation Models
• Large Language Models
• Generative AI frameworks such as langchain and crew.ai
• Unstructured data processing
• Qiskit
• Quantum computing theoretical foundations
• Algorithms like QAOA and sampling-based diagonalization algorithms
• AI Accelerator software stack
• Generative AI
• Transformers
• NLP
• AI Agents, tool use, function calling, reasoning models
• Techniques such as LoRA/PEFT, knowledge distillation, reinforcement learning, or RLHF/RLAIF (desirable)
• Watsonx.ai or similar platforms (preferred)
• Risks associated with AI deployments (familiarity with)
• Classical domain background for quantum applications (understanding of)
• Tensor networks
• Classical optimization (theory or SOTA heuristics)
• Computational chemistry
• Classical machine learning
• Quantum circuit optimization and error mitigation algorithms
• Java (standard coding skills)
• Containerization (Docker/Kubernetes/Openshift)
• Triton and Hugging Face (exposure to)
• AI Accelerator architectures
• NCCL and compilers

Qualifications

• Bachelor's Degree
• Master's Degree (preferred)
• Ph.D.
• Minimum 70% or 7.0 CGPA and above in the pursuing degree
• Strong coding skills
• Strong grasp of Data Structures, Algorithms, Problem Solving
• Solid foundation in Linear Algebra, Probability, and Statistics
• Hands-on experience with VS Code, Jupyter Notebooks, Git
• AI/ML Fundamentals
• Learning fundamentals and Data Manipulation/Analysis for large datasets
• Proficiency in AI libraries/frameworks: TensorFlow, PyTorch
• Experience in Deep Learning, Foundation Models, Large Language Models
• Familiar with Generative AI frameworks
• Expertise in unstructured data processing.
• Theoretical foundations in quantum computing.
• Strong coding skill in Qiskit.
• Ability to read research papers on quantum algorithms and applications and implement their methodology.
• Strong programming fundamentals in Python.
• Solid understanding of Machine Learning and Deep Learning.
• Familiarity with Large Language Models, Generative AI, Transformers and NLP.
• Hands-on experience with VS Code, Jupyter Notebooks, Git.
• Exposure to AI Accelerator software stack.
• Proficiency in AI libraries/frameworks: PyTorch.
• Exposure to AI Agents, tool use, function calling, reasoning models.
• Familiarity with techniques such as LoRA/PEFT, knowledge distillation, reinforcement learning, or RLHF/RLAIF is desirable.
• Ability to read, understand, and critically analyze research papers.
• Experience implementing ideas from research papers is highly desirable.
• Ability to reproduce research results and design-controlled experiments.
• Experience working with platforms such as Watsonx.ai or similar.
• Familiarity with risks associated with AI deployments, including agents going rogue.
• Understanding of classical domain background with respect to the quantum application being worked upon.
• Quantum circuit optimization and error mitigation algorithms, and their implementations on Qiskit.
• Standard coding skills in Java/Python (including pandas, NumPy), Data Structures, Algorithms, Problem Solving, linear algebra, probability, statistics.
• Experience with VS Code, Jupyter notebooks, Git.
• Exposure to popular RL libraries and hands on experience with RL training.
• Familiarity with concepts on linear algebra, probability, statistics and to fundamentals of LLM models.
• Git, Containerization (Docker/Kubernetes/Openshift).
• Exposure to Triton and Hugging Face.
• Exposure to AI Accelerator software stack.
• Familiarity of AI Accelerator architectures, NCCL and compilers.

Time Duration

• May to August 2027
• Maximum duration of 3 months