Deep Learning Research Engineer

📋 Description

• Research, Design, and Develop advanced Deep Learning models and novel Neural architectures.
• Explore architectures beyond Transformers, including State Space Models (SSMs), Liquid Neural
• Design and implement Agentic AI systems, Recursive Reasoning Engines, and tool-augmented
• Fine-tuning of models using PEFT (e.g. LoRA, QLoRA etc.), and Knowledge Distillation.
• Optimize model Performance, Inference Efficiency, Latency, and Memory utilization.
• Design rigorous experiments, evaluation frameworks, and benchmarks to validate research hypotheses.

🎯 Requirements

• M.Tech./M.S./Ph.D. in AI/ML or related disciplines, preferably from a Tier 1 premier Research
• 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
• Proficient in frameworks like PyTorch/JAX, with hands-on model development experience.
• Demonstrated ability to implement, reproduce, and extend research papers, including building neural
• Familiarity with distributed computing, GPU acceleration, and scalable ML infrastructure.

🎁 Benefits

• Publications at top venues (NeurIPS, ICML, ICLR, ACL) or notable open-source contributions in model
• Experience with LLM evaluation, fraud or anomaly detection, or building systems where the 'ground
• Hands-on work taking models from research into low-latency, production inference, such as