Job Summary
As a Applied AI Researcher, you will research and develop domain specific Large Language Models (LLMs) and Small Language Models (SLMs), retrieval systems, and scalable AI systems.
The role will focus on model training and fine tuning, retrieval and RAG, model optimization, evaluation, and high performance computing for AI. You will work on translating emerging research into scalable and enterprise ready AI solutions.
Key Responsibilities
• Conduct research in Generative AI, LLMs, SLMs, retrieval systems, and AI systems.
• Develop and optimize domain specific LLMs and SLMs using pre training, supervised fine tuning, preference tuning, reinforcement learning, and other model adaptation techniques.
• Design and improve retrieval and RAG systems using embeddings, hybrid retrieval, reranking, contextual chunking, and knowledge graphs.
• Develop evaluation approaches for models and retrieval systems covering quality, accuracy, reliability, latency, and cost.
• Research model optimization techniques such as quantization, pruning, distillation, sparsity, and efficient inference.
• Develop and optimize distributed model training and inference across multi GPU and multi node environments.
• Optimize AI workloads for GPU and HPC environments, focusing on throughput, latency, memory utilization, and compute efficiency.
• Explore agentic AI techniques such as reasoning, tool use, retrieval, reflection, and multi agent collaboration where relevant.
• Develop data preparation, synthetic data generation, benchmarking, and evaluation pipelines.
• Work with researchers, domain experts, and engineering teams to convert research ideas into reusable frameworks and solutions.
• Publish research findings in leading conferences and journals and contribute to patents and intellectual property.
• Mentor researchers and engineers and help build strong research capabilities within the team
Skill Requirements
• Strong research or engineering experience in LLMs, SLMs, retrieval systems, machine learning systems, or HPC for AI.
• Experience with model training and adaptation techniques such as supervised fine tuning, LoRA, QLoRA, preference tuning, reinforcement learning, or continual pre training.
• Strong understanding of retrieval and RAG, including embeddings, vector search, BM25, hybrid retrieval, reranking, chunking, and retrieval evaluation.
• Experience with distributed model training and inference using multi GPU or multi node environments.
• Experience with GPU and HPC technologies such as CUDA, GPU clusters, distributed computing, or performance optimization.
• Experience with PyTorch, Hugging Face, vLLM, SGLang, DeepSpeed, or similar frameworks.
• Strong programming skills in Python. Experience with C++ or GPU programming is an advantage.
• Demonstrated research capability through publications, patents, open source contributions, or applied research outcomes.
• Good written and verbal communication skills and the ability to work across research and engineering teams.
Other Requirements
• Ph.D. or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Computer Engineering, or a related field.