Deep Learning Research Engineer

HackerRank

. Research, design, and develop advanced deep learning models and novel neural architectures .

Posted 10/14/2026full-timeBengaluru • IndiaJuniorMid-LevelWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and developing advanced deep learning models and neural architectures, with a strong foundation in Linear Algebra, Probability, and Statistics. Proficient in optimizing model performance and implementing scalable AI systems using PyTorch or JAX.

Highest-signal resume keywords

Deep LearningMachine LearningPyTorchNeural Architecture DesignExperimental Design

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Deep LearningMachine LearningNeural Architecture DesignLinear AlgebraProbabilityStatisticsOptimizationModel EvaluationBenchmarkingKnowledge Distillation

Tools & Technologies

PyTorchJAXGPU AccelerationDistributed ComputingScalable ML Infrastructure

Certifications & Qualifications

M.Tech.M.S.Ph.D.

Industry Keywords

AIMLResearchAgentic AI SystemsRecursive Reasoning EnginesTool-Augmented ArchitecturesPEFT TechniquesLoRAQLoRAFraud Detection

Tech Stack

Tools & technologies

PyTorch

About the role

Key responsibilities & impact

Research, design, and develop advanced deep learning models and novel neural architectures
Explore architectures beyond Transformers, including State Space Models, Liquid Neural Networks, and hybrid approaches
Design and implement agentic AI systems, recursive reasoning engines, and tool-augmented architectures
Fine-tune models using PEFT techniques such as LoRA and QLoRA, and apply 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
Work across autonomous AI interviewing, fraud and suspicious-behavior detection, and technical-skill evaluation problems
Shift focus among open research problems based on highest-leverage work

Requirements

What you’ll need

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 PyTorch/JAX, with hands-on model development experience
Ability to implement, reproduce, and extend research papers, including building neural architectures from scratch
Familiarity with distributed computing, GPU acceleration, and scalable ML infrastructure
Understanding of experimental design, model evaluation, benchmarking, and statistical validation
Research aptitude demonstrated through publications or open-source contributions
Recruiters use @hackerrank.com email addresses
Must not require payment or credit check information to apply, interview, or work

Benefits

Comp & perks

Equal employment opportunity and affirmative action employer
Confidential handling of applicant information according to EEO guidelines