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HackerRank is reinventing the hiring platform for the agentic era, where AI/ML systems are core to the product, not auxiliary features. The company helps 3,000+ enterprises identify and invest in next-generation technical talent.
You will work on three core research problems reshaping the hiring stack:
**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. As candidates find new ways to game assessments, models must continuously evolve to keep pace.
**Evaluation**: Measuring technical skill in a world where AI writes code. Old proxies no longer hold; new ones are still being defined.
Your focus will shift across these problems based on where the highest-leverage work is at any given time.
**Key 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 (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
**Requirements:**
- M.Tech., M.S., or 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 or 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
**Nice-to-Have:**
- 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 ground truth is still being defined
- Hands-on work taking models from research into low-latency production inference (quantization, distillation, serving optimization)
