About Snorkel

Snorkel AI is the frontier AI data lab, helping teams build the data and environments behind high-performing frontier and agentic AI. We combine technology with research-driven AI data development to create datasets, benchmarks, evals, and custom solutions for real-world AI systems. Founded out of the Stanford AI Lab in 2019, Snorkel works with leading AI labs and enterprises to move from better data to better outcomes.

Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!

About the Role

Snorkel AI is hiring Forward Deployed Researchers (Multiple levels) who will partner with frontier AI labs on their most challenging data problems. This is a high-impact, customer-facing role that combines deep technical and research credibility with the ability to work directly with customer research teams. You'll partner with customer research teams to design complex data and environments that improve frontier model performance, serving as a thought partner throughout the engagement.

You'll work at the critical intersection of research, technical strategy, and customer partnership. This includes scoping training data needs, designing RL environments and tasks, developing evaluation frameworks, probing model behavior and failure modes, and translating customer research objectives into actionable technical plans.

Forward Deployed Researchers own a technical domain. You are the person who knows where frontier models fail in it, what data moves those numbers, and what we should build next.

Main Responsibilities

Own a technical domain end to end: the technical bar for what we build in it, and what we build next
Partner with frontier AI research labs to design datasets and environments that improve model performance
Lead technical conversations with customer researchers to understand model capabilities, failure modes, data requirements, and success criteria
Probe model behavior through systematic evaluation to uncover weaknesses and identify high-impact data interventions
Benchmark frontier models against our datasets to demonstrate value to customers, building or adapting evaluation harnesses as needed, and turn the results into pass-rate analysis, failure-mode breakdowns, and purchase recommendations
Design evaluation frameworks, calibration processes, and quality rubrics that establish measurable project success metrics
Develop technical specifications for data projects that balance research rigor with operational feasibility
Hold the demand shape for your domain: which labs want what, at what volume, against what bar, and where the asks are converging
Develop a point of view on where your domain is heading, and partner with Research on what should exist before a customer asks for it
Serve as thought partner to customer research teams throughout the sales cycle, building trust and credibility
Stay current on frontier AI research, RL environment design, post-training techniques, and evaluation methodologies

Preferred Qualifications

Strong expertise in frontier AI concepts including LLMs, training data pipelines, evaluation methodologies, post-training techniques (RLHF, DPO, RLAIF), and domain areas such as coding agents, reasoning, multimodal models, or RL environments
Experience in applied ML research, data science, or research-intensive technical roles
Customer-facing or collaborative research experience, or demonstrated aptitude for client engagement
Proficiency in Python, familiarity with ML frameworks and LLM APIs, and comfort running evaluations in containerized environments
Excellent communication skills, ability to deliver technical presentations and explain complex concepts to diverse audiences
Familiarity with data curation workflows, synthetic data generation, LLM-as-a-Judge, or evaluation framework design
Willingness to make and state a defensible assumption when a request arrives underspecified, rather than waiting for the customer to close the gap
Ability to work in a fast-moving environment, comfortable with ambiguity and rapid iteration