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Tier-1 brand and mid-level ML research role in a metro increases candidate density despite niche LLM specialization.
Specialized LLM training, agentic research, and evaluation expertise limit cross-industry transferability.
Explicit 3+ years, mandatory LLM training experience, PyTorch expertise, and demonstrated research impact create high shortlisting strictness.
Lead research on LLM mid-training and post-training techniques including continued pretraining, supervised fine-tuning (SFT), preference optimization, and reinforcement learning (RL).
Prototype and advance novel agentic AI architectures and algorithms covering planning, reasoning, memory, skills, tool use, retrieval, and multi-agent collaboration.
Design and develop rigorous experimentation frameworks, evaluation methodologies, and benchmarks to measure agent capabilities such as reasoning, planning, reliability, and safety, and translate failure analysis into research improvements.
3+ years of applied machine learning, deep learning, or AI research experience with independent project ownership.
Hands-on experience with LLM training and post-training methods including continued pretraining, SFT, preference optimization, or RL, and expertise in training data decisions and failure analysis at scale.
Strong proficiency in Python and advanced PyTorch with experience modifying models, training pipelines, or research infrastructure for experimentation.
Experience designing evaluation methodologies including benchmark design, trajectory-based evaluation, LLM-as-a-Judge, and human evaluation.
Researchers with demonstrated ability to independently identify novel problems, formulate hypotheses, and drive end-to-end research projects to impactful prototypes.
Expertise in agentic AI domains such as planning, reasoning, memory, skills, tool use, retrieval, and long-context processing.
Able to communicate research findings effectively to technical peers, engineers, and executives, and collaborate to transition research into production.