Deep Learning Research Engineer

Overview

Research, design and build advanced deep learning models and neural architectures for HackerRank's AI hiring platform, including the autonomous AI interviewer, integrity detection, and skill evaluation systems.

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.
• 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, open-source contributions etc.

Nice to have

• M.Tech./M.S./Ph.D. preferably from a Tier 1 premier research institution.
• Publications at top venues (NeurIPS, ICML, ICLR, ACL) or notable open-source contributions in model architectures or agentic systems.
• Experience with LLM evaluation, fraud or anomaly detection, or building systems where the ground truth is still being defined.
• Hands-on work taking models from research into low-latency, production inference, such as quantization, distillation, or serving optimization.

Details

• Location: Bangalore, India.
• Work mode: Hybrid.