AI Researcher Intern
Salary not listed • Onsite in Bangalore • Internship
Posted 3d ago
Remote Work Policy
On-site
Employment Type
Internship
Categories
AI Research Engineer
About the job
HackerRank is seeking research-oriented M.S./Ph.D. scholars to explore and develop novel Deep Learning architectures and build efficient, trustworthy AI systems. A key research direction involves developing a model-agnostic external Recursive Reasoning Engine to enable smaller models to solve complex software engineering tasks with greater effectiveness and lower inference cost. This role will focus on advancing AI-driven Technical Interviewing, assessment of Integrity, and the measurement of Engineering skill in the agentic era, tackling open problems in building autonomous AI interviewers, detecting fraud, and measuring technical skill in a world where AI assists in coding.
Responsibilities
- 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 techniques like LoRA and QLoRA, and apply 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.
Requirements
- 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.
- Proficiency 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 or open-source contributions.