Distyl AI develops AI native technologies for humans & AI to collaborate to power the operations of the Global Fortune 1000.

In just 24 months, we’ve rapidly grown to partner with some of the world’s largest enterprises—including F100 telecom, healthcare, manufacturing, insurance, and retail companies—delivering multiple AI deployments with $100M+ impact. Our platform, Distillery, along with our team of AI Engineers, Researchers, and Strategists, is pioneering AI-native systems of work, solving the most complex, high-stakes challenges at scale.

Distyl is founded and led by proven leaders from companies like Palantir, Apple, and top national laboratories. We work in deep partnership with OpenAI, jointly going-to-market at the largest enterprises and collaborating evaluating and testing the latest models. Backed by Lightspeed, Khosla, Coatue, industry leaders like Nat Friedman (former GitHub CEO), as well as board members of over 20+ F500s, Distyl is building the future of AI-powered enterprise operations.

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.
• Experience Building with Models, Not Just Building Models: 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.).

What We Offer

• An opportunity to advance the cutting edge of LLM research and directly revolutionize work in the enterprise space.
• Ownership of high-impact research projects, with the autonomy to explore novel approaches and solutions.
• Access to state-of-the-art AI models, real business problems, and proprietary data sets across a diverse range of real-world industries.
• Competitive salary and benefits package, including equity options, medical/dental/vision covered at 100% for you and your dependents, 401K plan, and perks such as commuter benefits and lunch provided in office.
• Be part of a mission-oriented company to create practical adoption during the biggest revolution in human productivity.
• A collaborative and intellectually stimulating environment that encourages innovation and personal growth.

If you are an innovative, ambitious, and driven individual looking to make a difference in the world of AI, we want to hear from you. Apply now to join our team as an Applied AI Researcher and help us shape the future of AI-driven solutions for enterprises across the globe.

Note: Distyl is a hybrid working environment and requires in office collaboration 3 days a week. We have offices in SF and NYC