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Protocol Intelligence
Data-driven signals on your job's competitivenessMid-level requirements, metro location, and recognizable employer make applicant competition moderate despite niche LLM specialization.
Highly specialized LLM research skills limit cross-industry transferability.
Explicit 5+ years, mandatory LLM training and PyTorch expertise make filters highly stringent.
Job Description
Structured overview of role & requirementsAbout This Role
Lead research on LLM mid-training and post-training processes such as continued pretraining, SFT, preference optimization, and RL, focusing on training data and its effect on agent behavior.
Research and prototype novel agent architectures and algorithms covering planning, reasoning, memory, skills, tool use, retrieval, and multi-agent collaboration, advancing current methods.
Design and implement research harnesses and evaluation methodologies, including novel benchmarks for agent capabilities and safety, ensuring reproducibility, systematic experimentation, and rigorous comparison across models and architectures.
Minimum Requirements
5+ years of experience in machine learning, deep learning, AI research, or related field with independent applied research experience.
Hands-on experience with LLM training and post-training processes including at least one of continued pretraining, SFT, preference optimization, or RL.
Strong Python and advanced PyTorch skills with experience modifying models, training pipelines, or research infrastructure.
Experience designing evaluation methodologies such as benchmark design, trajectory-based, LLM-as-a-Judge, and human evaluations for AI research.
Ideal Candidate Profile
Deep practical expertise in agentic AI capabilities including planning, reasoning, memory, tool use, retrieval, long-context processing, and knowledge grounding.
Demonstrated ability to independently identify key research problems, formulate hypotheses, conduct rigorous experiments, and deliver validated prototypes with measurable impact.
Comfortable communicating complex research findings effectively to technical teams and executive stakeholders and transitioning research prototypes into production.