Employer description & source details
HackerRank is advancing AI-driven Technical Interviewing, assessment of Integrity, and the measurement of Engineering skill to use AI assistance effectively in the agentic era. We are looking for 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 is to develop a model-agnostic external Recursive Reasoning Engine that enables smaller models (SLM/SSM/Hybrid etc.) to solve complex, repository-grounded software engineering tasks with greater effectiveness, traceability, and lower inference cost. The agentic era is reshaping every layer of the hiring stack. These are some of the core problems you’ll be working across, none of them fully solved. 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. The ways candidates game assessments change frequently, and the models need to keep up. Evaluation: Measuring technical skill in a world where AI writes the code. The old proxies no longer hold and the new ones have not been defined yet. Your focus will shift across these depending on where the highest-leverage work is at any given time. 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-tuning of models using PEFT (e.g. LoRA, QLoRA etc.), and Knowledge Distillation Optimize model Performance, Inference Efficiency, Latency, and Memory utilization.
Extracted skills: machine learning