Deep Learning Research Engineer
HackerRank (company site, opens in a new tab)2–3 yrsBengaluruHybridDirect career pageEst. 18–30 LPA
Posted 8h ago
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Reach people at HackerRank
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at HackerRank you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Research, design, and develop advanced Deep Learning models and novel neural architectures including beyond Transformers (SSMs, LNNs, Hybrid).
- Design and implement Agentic AI systems, Recursive Reasoning Engines, and tool-augmented architectures.
- Optimize model performance, inference efficiency, latency, memory utilization, and translate research into scalable, production-grade AI systems.
Minimum Requirements
- M.Tech./M.S./Ph.D. in AI/ML or related disciplines from a Tier 1 premier Research Institution preferred.
- 2-3 years of experience in Deep Learning, Machine Learning, or Applied AI Research.
- Proficient in frameworks such as PyTorch or JAX with hands-on model development experience.
- Strong foundations in Linear Algebra, Probability, Statistics, Optimization, and Deep Learning algorithms.
Ideal Candidate Profile
- Has research aptitude demonstrated via publications or notable open-source contributions in topology relevant to deep learning or agentic AI systems.
- Comfortable exploring next-generation AI architectures and solving open-ended research problems with first-principles thinking and rigorous experimentation.
- Experienced in taking models from research to production including optimization techniques such as quantization, knowledge distillation, and serving optimization.