Deep Learning Research Engineer

🏢 Hackerrank · 📍 Hybrid in Bangalore, India · 💼 hybrid

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Position Summary & Responsibilities

HackerRank helps companies like NVIDIA, Amazon, and Microsoft hire and upskill the next generation of developers based on skills, not pedigree. Our platform is trusted by over 2,500 of the world’s most innovative companies to build strong engineering teams ready for what’s next.

Software has entered an era where humans and AI build side by side. As this shift accelerates, the definition of strong technical talent is changing. We give companies better ways to identify and invest in next-generation skills.

People at HackerRank care deeply about the impact of their work and sweat the small details so our customers can be wildly successful with products they genuinely love to use. We move with urgency and believe great outcomes come from high standards.

About the role

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.

Open Problems

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.

What you will 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-tuning of models using PEFT (e.g. LoRA, QLoRA etc.), and Knowledge Distillation.
Optimize model Perfor

🎯 Candidate Preparation Guide For Hackerrank Applicants

Key Role Competencies & Evaluation Highlights

Key Role Focus:

Evaluators heavily test your technical depth, data-driven approach to developer productivity, and ability to scale enterprise-grade SaaS ecosystems.

📋 Preparation Priorities (Key Areas to Clarify):

1. Proposing overly complex assessment models that create high candidate friction
2. Hand-waving technical architecture questions or lacking system design depth
3. Failure to understand the nuances of bias reduction in technical screening

✨ Recommended Role Alignment Hook:

I scale technical hiring platforms that accurately measure engineering talent while prioritizing an elite candidate experience.

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