Applied AI Researcher, Post-Training

Key Responsibilities

• Develop and evaluate techniques such as supervised fine-tuning, preference optimization (DPO, RLHF, RLAIF), and continual adaptation to align models with Distyl’s enterprise systems.
• Investigate new methods for aligning large models with human and system-level objectives.
• Inform how Distyl leverages foundation models safely, effectively, and at scale across industries.

What We Require

• Deep Understanding of Post-training Techniques: Familiarity with supervised fine-tuning, preference optimization (RLHF/DPO), LoRA/PEFT, and instruction-tuning pipelines.
• Experience Adapting Frontier Models: Tuned or adapted LLMs/SLMs to specialized domains or behaviors through data curation, reward modeling, or continual pretraining.
• Experience Building with Models, Not Just Building Models: Develop intelligent systems using models rather than training or fine-tuning them.
• Expertise in Compound AI Systems, Agentic Collaboration, and Associated Techniques (Ensembling, ReAct, Graph-of-Thoughts, etc.).
• Proven Track Record of Research Results: Published in top journals, posted amazing work on Twitter, or elsewhere.
• Uses AI Every Day: Using tools like ChatGPT, Cursor, and Perplexity to accelerate workflow.
• Strong Programming and Data Analysis Skills: Building prototypes and performing experiments to prove effectiveness.

What We Offer

• Base Salary Range: $150K – $250K, depending on experience, location, and level.
• Meaningful Equity and Comprehensive Benefits Package: Includes 100% coverage of medical, dental, and vision insurance, flexible time off, retirement and financial planning benefits, wellness benefits, and state-of-the-art AI models.
• Mission-Driven Culture: Fast-moving environment that values curiosity, pragmatism, and excellence.