HackerRank helps companies like NVIDIA, Amazon, and Microsoft hire and upskill developers based on skills, not pedigree.
Trusted by over 2,500 of the world's most innovative companies.
The AI/ML systems that power the platform are the product.
Open Problems
Chakra: building an autonomous AI interviewer that conducts, adapts to, and evaluates technical interviews end to end.
Integrity: detecting fraud and suspicious behavior across multiple signal types.
Evaluation: measuring technical skill in a world where AI writes the code.
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 (e.g. LoRA, QLoRA) and Knowledge Distillation.
Optimize model performance, inference efficiency, latency, and memory utilization.
Design rigorous experiments, evaluation frameworks, and benchmarks.
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.
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 or open-source contributions.
Even better: publications at NeurIPS, ICML, ICLR, ACL; experience with LLM evaluation, fraud or anomaly detection; hands-on work taking models into low-latency production inference (quantization, distillation, serving optimization).