About Distyl AI

Distyl is an applied AI technology company partnering with the world’s most ambitious institutions to rearchitect critical operations for the frontier of AI. Our customers include the largest companies in telecom, healthcare, insurance, manufacturing, consumer goods, and global social organizations.

We research and deploy technologies that power AI-native operations — both for our partners and for Distyl itself. Our work spans research into self-constructing systems, the development of the most reliable execution of AI systems, and products that transform mission-critical workflows. As a result, Distyl's technologies affect some of the world's largest operations — from hundreds of millions of consumer interactions to tens of millions of supply chain transactions and millions of patient journeys.

Distyl is backed by leading investors including Lightspeed Venture Partners, Khosla Ventures, Coatue, DST Global, and the board-members of 20+ F500s.

What We Are Looking For

At Distyl we’re pushing the envelope of AI utilization in enterprise. This requires creative researchers who don’t just want to drive incremental improvements on benchmarks or optimize an existing process but instead are looking to creatively redefine how software is used.

Our researchers come from many academic backgrounds but have strong research track records, operate in an AI-native way, and would be bored staying on the rails of a traditional research org.

Key Responsibilities

• The Post-Training team focuses on adapting foundation models to real-world performance and alignment requirements. Researchers 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. The goal is to bridge raw model capability with trustworthy, contextually aligned system behavior
• Researchers in Post-Training investigate new methods for aligning large models with human and system-level objectives. They explore trade-offs between generalization and specialization, data efficiency and robustness, capability and controllability. Their work informs 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: You’ve tuned or adapted LLMs/SLMs to specialized domains or behaviors through data curation, reward modeling, or continual pretraining.
• : We develop intelligent systems using models rather than training or fine-tuning them. Ideal candidates have expertise in compound AI systems, agentic collaboration, and associated techniques (ensembling, ReAct, graph-of-thoughts, etc.).