Deep Learning Research Engineer
HackerRank8 hours ago
Bengaluru, IndiaMid Level
Responsibilities
- 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 methods such as LoRA and QLoRA, and apply knowledge distillation.
- Optimize model performance, inference efficiency, latency, and memory utilization.
- Design experiments, evaluation frameworks, and benchmarks to validate research hypotheses.
- Translate cutting-edge research into scalable, production-grade AI systems.
Requirements
- M.Tech., M.S., or Ph.D. in AI/ML or a related discipline, preferably from a 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.
- Proficiency with frameworks such as PyTorch or JAX and 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 machine learning infrastructure.
- Understanding of experimental design, model evaluation, benchmarking, and statistical validation.
- Demonstrated research aptitude through publications or open-source contributions.
- Preferred experience includes publications at venues such as NeurIPS, ICML, ICLR, or ACL; LLM evaluation; fraud or anomaly detection; and taking models into low-latency production inference through quantization, distillation, or serving optimization.
Tech Stack
Categories
AI ResearchML Engineering
About HackerRank
HackerRank builds a developer skills assessment and technical interview platform used by recruiters and hiring managers to evaluate engineers via coding tests, take-home challenges, and pair-programming sessions. It sells its software to enterprises on a subscription basis and maintains a large developer practice community; the privately held company was founded in 2012, is headquartered in Santa Clara, and counts organizations such as OpenAI, NVIDIA, and Amazon among its customers.