AI Researcher

Role Overview: XenonStack is looking for a passionate AI Researcher with expertise in machine learning, reinforcement learning, and large language models (LLMs) to contribute to their research in Agentic AI systems. As an AI Researcher, you will collaborate with product and engineering teams to advance next-generation architectures, reasoning models, and reinforcement-based optimization for deploying and trusting AI agents in enterprise contexts. Key Responsibilities: - Conduct applied research on large language models, reasoning systems, and multi-agent architectures. - Explore and prototype reinforcement learning, retrieval-augmented generation (RAG), and hierarchical memory systems for enterprise applications. - Design, run, and evaluate experiments to enhance context management, generalization, and reasoning accuracy. - Collaborate with engineering teams to translate research into deployable prototypes. - Build and assess agentic frameworks and orchestration strategies using LangChain, LangGraph, or equivalent. - Develop and test AI safety and interpretability techniques to ensure ethical and reliable model behavior. - Work on integrating research advancements into real-world AI applications and products. - Present findings in internal research forums and contribute to thought leadership. - Stay updated with emerging research in LLMs, RLHF, multimodal AI, and agentic reasoning. Qualifications Required: Must-Have: - Masters or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or related field. - 3-6 years of research experience in AI, ML, or NLP domains. - Strong expertise in deep learning frameworks (PyTorch, TensorFlow, JAX). - Experience with LLMs, Transformers, RAG pipelines, and reinforcement learning (RLHF, RLAIF). - Proficiency in Python and ML research tooling (Weights & Biases, Hugging Face, LangChain). - Ability to design, conduct, and analyze experiments with rigor. Good-to-Have: - Research experience in multi-agent systems, symbolic reasoning, or causal inference. - Understanding of evaluation metrics for LLMs and AI safety principles. - Contributions to AI research papers, open-source projects, or conferences (NeurIPS, ICLR, ICML, ACL). - Familiarity with distributed training and optimization at scale. (Note: Additional details about the company culture have been omitted from the JD as they are not directly related to the job role.),