Senior Deep Learning Research Engineer

HackerRank

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

Posted 11/12/2026full-timeBengaluru • IndiaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in designing and developing advanced deep learning models and architectures, with a strong foundation in Linear Algebra, Probability, and Statistics. Proven ability to translate research into scalable AI systems and optimize model performance for production environments.

Highest-signal resume keywords

Deep LearningMachine LearningPyTorchModel EvaluationExperimental Design

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Neural Architecture DevelopmentState Space ModelsLiquid Neural NetworksPEFTKnowledge DistillationOptimizationStatistical ValidationGPU AccelerationQuantizationServing Optimization

Tools & Technologies

JAXDistributed ComputingScalable ML Infrastructure

Certifications & Qualifications

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

Industry Keywords

Agentic AI SystemsFraud DetectionAnomaly DetectionTechnical-Skill EvaluationResearch Publications

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 (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 such as LoRA and QLoRA, 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
Work across autonomous AI interviewing, fraud and suspicious-behavior detection, and technical-skill evaluation problems

Requirements

What you’ll need

M.Tech./M.S./Ph.D. in AI/ML or related disciplines
3-7 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.
Publications at top venues or notable open-source contributions in model architectures or agentic systems
Experience with LLM evaluation, fraud or anomaly detection, or systems where the ground truth is still being defined
Hands-on work taking models from research into low-latency, production inference, such as quantization, distillation, or serving optimization