Applied AI Researcher, Post-Training
Develops and evaluates post-training techniques like supervised fine-tuning, RLHF/DPO, and continual adaptation to align foundation models with enterprise systems. Requires expertise in adapting LLMs/SLMs, compound AI systems, and strong prototyping skills.
Key Responsibilities
- Adapt foundation models to real-world performance and alignment requirements using supervised fine-tuning, preference optimization (DPO, RLHF, RLAIF), and continual adaptation.
- 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.
Requirements
- Deep understanding of post-training techniques: supervised fine-tuning, preference optimization (RLHF/DPO), LoRA/PEFT, instruction-tuning pipelines.
- Experience adapting frontier models (LLMs/SLMs) to specialized domains via data curation, reward modeling, or continual pretraining.
- Expertise in compound AI systems, agentic collaboration (ensembling, ReAct, graph-of-thoughts).
- Proven research track record (publications, public work).
- Daily use of AI tools (ChatGPT, Cursor, Perplexity).
- Strong programming and data analysis skills for prototyping and experiments.
What We Offer
- Base salary: $150K–$250K (depending on experience, location, level).
- Equity, comprehensive benefits: 100% covered medical/dental/vision, 401(k), commuter benefits, in-office lunch.
- Access to state-of-the-art models and AI tools.
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