AI Solutions Engineer - Data as a Service

About Snorkel AI

At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data. We’re on a mission to help enterprises transform expert knowledge into specialized AI at scale. The AI landscape has gone through incredible changes, but one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!

About The Role

Snorkel AI is hiring an AI Solutions Engineer - Data as a Service who will partner with leading AI labs on their most challenging data problems. This is a high-impact, customer-facing role that combines technical depth with strong presales instincts. You'll partner with customer research teams to design complex data and environments that improve frontier model performance, demonstrating Snorkel's capabilities through research-driven engagements.

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. You'll develop technical specifications, analyze frontier model failure modes, and serve as a thought partner to customer research teams throughout the sales cycle and into early delivery phases.

Main Responsibilities

• 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.
• 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.
• 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 with customer-facing or collaborative research experience.
• Proficiency in Python and familiarity with ML frameworks and LLM APIs.
• 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.
• Ability to work in a fast-moving environment, comfortable with ambiguity and rapid iteration.
• B.S. in Computer Science, Machine Learning, or related field with 4+ years of experience in AI/ML solutions engineering or technical customer-facing roles.

Why Join Snorkel AI?

At Snorkel AI, we're building the future of data-centric AI. Our Expert Data-as-a-Service organization partners with world-class customers to solve some of the hardest data challenges — creating training and evaluation data that power the next generation of LLMs and AI systems. You'll work directly on projects that impact real production systems, while shaping how internal teams deliver faster, better, and more intelligently. This is a rare opportunity to own technical data workflows and be a founding member of the technical DaaS team.

Key skills/competency

• Frontier AI
• LLMs
• Data Pipelines
• Evaluation Methodologies
• RLHF/DPO/RLAIF
• Python
• Machine Learning Frameworks
• Customer Engagement
• Technical Strategy
• Data Curation