AI Researcher Intern

Overview

Research-oriented M.S./Ph.D. scholar role to explore and develop novel Deep Learning architectures and build efficient, trustworthy AI systems, including a model-agnostic external Recursive Reasoning Engine for smaller models on repository-grounded software engineering tasks.

What you'll do

• Research, design, and develop advanced Deep Learning models and novel neural architectures.
• Explore architectures beyond Transformers, including State Space Models (SSMs), Liquid Neural Networks (LNNs), and Hybrid approaches.
• Design and implement Agentic AI systems, Recursive Reasoning Engines, and tool-augmented architectures.
• Fine-tune models using PEFT (e.g. LoRA, QLoRA etc.) and Knowledge Distillation.
• Optimize model performance, inference efficiency, latency, and memory utilization.
• Design rigorous experiments, evaluation frameworks, and benchmarks to validate research hypotheses.
• Translate cutting-edge research into scalable, production-grade AI systems.

What you'll need

• M.Tech./M.S./Ph.D. in AI/ML or related disciplines.
• 2-3 years of experience in Deep Learning, Machine Learning, or Applied AI Research.
• Strong foundations in Linear Algebra, Probability, Statistics, Optimization, and Deep Learning algorithms.
• Proficient in frameworks like PyTorch/JAX, with hands-on model development experience.
• Demonstrated ability to implement, reproduce, and extend research papers, including building neural architectures from scratch.
• Strong understanding of experimental design, model evaluation, benchmarking, and statistical validation.
• Demonstrated research aptitude through publications, open-source contributions etc.

Nice to have

• Understanding of evaluation benchmarks for software engineering tasks and human-agent collaborations.
• Familiarity with distributed computing, GPU acceleration, and scalable ML infrastructure.

Details

• Onsite in Bangalore.