Deep Learning Research Engineer

Hiring is one of the most consequential decisions a company makes. 3,000+ enterprises rely on HackerRank to get it right. We are now reinventing how that works for the agentic era. The AI/ML systems that power this platform are not auxiliary features. They are the product.

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.
• Design rigorous experiments, evaluation frameworks, and benchmarks to validate research hypotheses.
• Translate cutting-edge research into scalable, production-grade AI systems.
• 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, open-source contributions etc.

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