Applied AI Researcher, Post-Training
Core
Adapting foundation models to real-world performance and alignment requirements for enterprise systems.
Role type
Applied AI Researcher (Post-Training)
Builds
Intelligent systems using models, agentic collaboration, and compound AI systems for enterprise clients.
Domain
Enterprise AI, Large Language Models, Post-Training
Deliverable
production ML models
Required skills
supervised fine-tuning, preference optimization (DPO, RLHF, RLAIF), LoRA/PEFT, instruction-tuning, data curation, reward modeling, continual pretraining, compound AI systems, agentic collaboration, ensembling, ReAct, graph-of-thoughts
Preferred skills
experience building prototypes, using AI tools daily (ChatGPT, Cursor, Perplexity), strong programming and data analysis skills
Technologies
LLMs, SLMs, DPO, RLHF, RLAIF, LoRA, PEFT
Responsibilities
Develop and evaluate techniques to align models with enterprise systems; investigate methods for aligning large models with human and system-level objectives; explore trade-offs between generalization and specialization, data efficiency and robustness, capability and controllability.
Seniority
Mid-to-Senior, hands-on IC
