Deep Learning Research Engineer
Salary not listed • Hybrid in Bangalore, India • Full-time
Posted 17d ago
Job Location
Hybrid in Bangalore, India
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
AI Research Engineer
About the job
HackerRank is reinventing the hiring process for the agentic era, where AI systems are not just features but the core product. This role focuses on building the AI/ML systems that power the platform, tackling complex, unsolved problems in areas like autonomous AI interviewing, fraud detection, and skill evaluation in the age of AI-generated code. You will work across these critical areas, with your focus shifting to where the highest-leverage work is needed at any given time. This is an opportunity to contribute to a fast-paced environment with intellectual freedom and ownership, turning novel AI research into efficient, scalable, real-world systems.
Responsibilities
- Research, design, and develop advanced Deep Learning models and novel neural architectures, exploring beyond Transformers to include 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, preferably from a Tier 1 premier research institution.
- 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.
- Familiarity with distributed computing, GPU acceleration, and scalable ML infrastructure.
- Strong understanding of experimental design, model evaluation, benchmarking, and statistical validation.
- Demonstrated research aptitude through publications or open-source contributions.