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IBM Research/Research Software - Intern

IBM

  • Bengaluru, India
  • Rs 15,000 per month (stipend)
  • Internship
  • Posted Today
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About the role

IBM India, a subsidiary of the global technology giant IBM, has been operating in the country since 1992. With a portfolio that spans cloud, AI, quantum computing, blockchain, and consulting, IBM helps enterprises across sectors such as finance, healthcare, automotive, and government to transform their operations. The company’s research arm, IBM Research – India, is home to world‑class labs in Gurgaon and Bangalore where cutting‑edge work on quantum algorithms, AI agents, and next‑generation data platforms is carried out.

IBM Software Innovation Lab (SIL) India, another key unit, translates these breakthroughs into scalable software products that power IBM’s hybrid cloud and AI offerings.

The Internship Program is part of IBM’s University Relations initiative and is designed for bright engineering and science students. Interns are placed in either the Software Innovation Lab (AI & Data) or IBM Research (Quantum Algorithms or AI) and work on real‑world projects under the mentorship of senior researchers and engineers. The role offers a blend of hands‑on coding, algorithm design, and research paper implementation, giving interns exposure to enterprise‑grade AI pipelines, quantum hardware, and large‑scale data processing.

Key Responsibilities

  • Develop and prototype AI/ML models for enterprise automation using Python, TensorFlow or PyTorch.
  • Implement quantum algorithms (e.g., QAOA, sampling) on IBM Quantum hardware via Qiskit.
  • Conduct literature reviews, reproduce research results, and propose enhancements.
  • Build data pipelines and perform large‑scale data manipulation with Pandas and NumPy.
  • Contribute to the design of agentic AI systems, including prompt engineering and tool‑use frameworks.
  • Write clean, version‑controlled code using Git and collaborate through VS Code/Jupyter notebooks.
  • Participate in code reviews, sprint planning, and daily stand‑ups with the lab team.
  • Document experiments, results, and insights for internal knowledge bases.
  • Assist in performance benchmarking of AI models and quantum circuits.
  • Explore emerging technologies such as LangChain, crew.ai, and AI accelerator stacks.

Tech Stack: Python, Pandas, NumPy, TensorFlow, PyTorch, Qiskit, VS Code, Jupyter, Git, LangChain, Watsonx.ai, AI accelerator SDKs, Docker/Kubernetes (optional).

Growth Path: Successful interns are often offered full‑time roles as Software Engineers, Research Scientists, or Quantum Engineers. IBM provides a clear career ladder, continuous learning programs, and opportunities to work on global projects.

Why Join IBM? IBM offers a world‑class learning environment, access to state‑of‑the‑art quantum computers, and the chance to contribute to AI solutions that impact Fortune 500 customers. The hybrid work model, strong mentorship culture, and emphasis on diversity and inclusion make it an ideal launchpad for fresh talent.

Eligibility & process

Who can apply

Eligible candidates must be pursuing B.E./B.Tech, M.E./M.Tech (including dual degree) or Ph.D. in Computer Science, Electrical Engineering, Mathematics, or related fields. Minimum CGPA 7.0/10 (or 70%) in the current degree. No active backlogs at the time of application. Internship period is May‑August 2027 for up to 3 months.

Skills & stack

Python Pandas NumPy TensorFlow PyTorch Qiskit AI Machine Learning

Selection process

  1. Round 1: Online coding assessment (Python/DSA)
  2. Round 2: Technical interview (project discussion, AI/Quantum concepts)
  3. Round 3: HR interview (fit, motivation, internship logistics)

Prep kit

19 resources
Study material 4
Company-specific guides 4
Likely coding questions 4
Round-wise strategy 4
Round 1: Online Assessment (OA) Aptitude, Quantitative Logic & 2 Coding Problems Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1 Core Tech Stack (Python, Pandas, NumPy, TensorFlow, PyTorch, Qiskit, AI, Machine Learning) & Live Code Tracing Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving Database Schemas, APIs & Architecture Basics Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion IBM Core Values, Learning Agility & Offer Terms Demonstrate passion, strong communication, and readiness for full-time collaboration.
Technical questions 4
What is the bias-variance tradeoff and how do you prevent overfitting? Answer
Model answer: High bias leads to underfitting (oversimplified model), high variance leads to overfitting (captures noise). Mitigate using L1/L2 Regularization, Dropout, Cross-Validation, and data augmentation.
Explain the difference between Precision, Recall, and F1-Score. Answer
Model answer: Precision = TP / (TP + FP) (correctness of positive predictions). Recall = TP / (TP + FN) (coverage of actual positives). F1-Score is the harmonic mean of Precision and Recall.
How does Gradient Descent work and what is the role of Learning Rate? Answer
Model answer: It optimizes loss functions by iteratively moving weights in the direction of negative gradient. A large learning rate may overshoot the minimum; a small rate causes slow convergence.
What is the difference between Supervised, Unsupervised, and Self-Supervised learning? Answer
Model answer: Supervised uses labeled data (X -> y). Unsupervised finds hidden patterns in unlabeled data (clustering/PCA). Self-supervised generates labels from input data (e.g. masked language modeling in BERT/Transformers).
Behavioural & HR questions 3
Why do you want to join IBM as a IBM Research/Research Software - Intern?
Highlight IBM's market reputation, recent tech innovations, and how your skills in Python directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
IBM Rs 15,000 per month (stipend)
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