Snorkel AI

AI Advocate role focused on open-source and research communities, acting as the primary technical voice for Snorkel AI. Responsibilities include creating technical content, leading open-source efforts, advancing AI evaluation and benchmarking, driving conference presence, and partnering with research teams. The role emphasizes translating Snorkel's data-centric AI methodology into practical applications and community engagement.

Research Scientist role focused on designing datasets for frontier model training and evaluation, translating benchmark insights, and staying at the forefront of LLM evaluation research. This is a customer-facing role that collaborates cross-functionally and influences company roadmap and external research.

Research Scientist role focused on reinforcement learning for training and aligning large language models, with a mission to transform expert knowledge into specialized AI at scale for enterprises. The role involves researching and implementing RL techniques, designing data pipelines for training signals, prototyping RL training recipes, and contributing to customer-ready data products.

Research Scientist at Snorkel AI focused on designing, implementing, and validating novel AI techniques for data development, such as synthetic data generation using LLM as a Judge. The role involves prototyping and building end-to-end workflows, integrating research into scalable systems, and collaborating with partners to test solutions in applied settings. Emphasis on rapid iteration and driving innovation from research into production.

The role focuses on building and operating the infrastructure for model training and evaluation, including GPU clusters, training pipelines, and orchestration systems. It involves managing ML frameworks, experiment tracking, and ensuring reliable, scalable execution of training jobs.

This role focuses on partnering with frontier AI research labs to design datasets and environments that improve model performance. It involves scoping training data needs, designing RL environments, developing evaluation frameworks, probing model behavior, and translating research objectives into technical plans. The role is customer-facing and requires strong technical and research credibility.

Research Scientist role focused on designing, implementing, and validating novel AI techniques for data development, such as synthetic data generation using LLM as a Judge. The role involves prototyping and building end-to-end workflows, integrating research ideas into scalable systems, and collaborating with partners to productionize prototypes. Emphasis on applied research and system-building in an enterprise AI context.

Senior AI/ML Engineer to join the AI Platform team, focusing on distributed systems, LLM infrastructure, synthetic data generation, and agentic orchestration. The role involves architecting the core platform, designing APIs and compute services, generating synthetic data, creating and evaluating agentic systems, provisioning RL training environments, deploying benchmarks and labeling pipelines, and running LLMs efficiently.

Director, Forward Deployed Researcher on the Data-as-a-service team. This player-coach role involves people leadership (hiring, coaching) and hands-on technical work on strategic accounts, focusing on data problems for frontier AI labs. Responsibilities include scoping training data needs, designing RL environments, developing evaluation frameworks, and translating research objectives into technical plans. The role requires partnering with sales leadership and cross-functional teams, setting technical bars, and building scalable processes.

Lead a forward-deployed engineering team focused on building and delivering high-quality datasets for AI initiatives. This involves defining data quality, implementing workflows, enhancing human-in-the-loop techniques, and owning systems for scalable data delivery. The role sits at the intersection of data engineering, ML engineering, and customer engagement, requiring strong leadership and hands-on technical skills in LLM-based workflows.

Senior Product Manager at Snorkel AI to lead development for agentic data systems, synthetic data systems, evaluations, and data-acceleration initiatives. The role involves defining product vision, roadmap, and execution plans for intelligent workflow systems, optimizing data pipelines, and improving task quality. Key responsibilities include owning the product vision for task and evaluation products, leading evaluation initiatives, defining performance metrics, building recommendation systems for task matching, and supporting enablement efforts.

Snorkel AI is seeking a Senior Manager of Research to lead a team focused on data-centric research for next-generation foundation models. The role involves team building, frontier data research, strategic roadmapping for AI data bottlenecks, and operational excellence. The ideal candidate has 5+ years in applied AI/ML research, 4+ years managing technical teams, and practical experience with LLMs and agentic workflows.

Applied AI Engineer at Snorkel AI, focusing on building and deploying Gen AI and ML solutions for customers, leveraging Snorkel Flow or custom approaches. Responsibilities include developing RAG, fine-tuning pipelines, prompt engineering, agentic workflows, creating datasets and evaluation workflows, and collaborating with customers and product teams. The role emphasizes bridging AI technology with business value and standardizing solutions into platform capabilities.

Applied AI Engineer role focused on building and deploying Gen AI and ML solutions for enterprise customers, leveraging Snorkel Flow or custom approaches. Responsibilities include customer partnership, developing RAG, fine-tuning, prompt engineering, and agentic workflows, creating datasets and evaluation workflows, and standardizing solutions into reusable recipes and platform capabilities. The role emphasizes a data- and evaluation-first mindset and bridging the gap between AI technology and business value.

This role leads high-stakes Data-as-a-Service programs end-to-end, acting as the CEO of the program. It involves owning the P&L, managing stakeholders (clients, internal teams, expert contributors), designing training data programs, developing and scaling the contributor network, building systems from scratch, monitoring performance, driving communications, managing data flows, and operating with urgency to ship better models. The role requires ML intuition for training data quality and evaluation design, and strong operational skills for managing complex programs at scale.

Senior Full Stack Engineer to build and maintain an Expert Data Collection Platform that enables domain experts to provide high-signal data for training and refining AI models. The role involves end-to-end feature development, scalable service optimization, and user-focused interface design.

Manager for Forward Deployed Engineering (FDE) within the Data-as-a-Service (DaaS) organization. This role leads a team responsible for the technical execution of DaaS delivery, owning systems, workflows, and quality frameworks for high-quality dataset production at scale. Responsibilities include building and leading the FDE function, defining its operating model and roadmap, hiring and mentoring engineers, owning AI data pipelines (generation, evaluation, quality), driving ML-assisted and HITL workflows, establishing measurement and benchmarking systems, and developing internal tooling. The role requires strong hands-on technical depth in AI/ML/GenAI engineering, experience leading engineers, building/operating production ML/LLM workflows, proficiency in Python/SQL, and experience with evaluation frameworks.

Partner with Sales to lead technical discovery, solution scoping, and demo development for GenAI and ML solutions, translating customer needs into technical proposals and driving adoption of AI.

Snorkel AI is seeking a Forward Deployed Engineer to work on AI/ML data products for enterprise clients. This role involves end-to-end ownership of the AI data pipeline lifecycle, including developing and deploying ML-based workflows, building HITL data generation and review processes, generating synthetic datasets, and packaging production-grade datasets. The engineer will also define production specifications, build evaluators, design quality measurement systems, and perform custom model benchmarking.

Snorkel AI is seeking a Technical Delivery Manager (TDM) to lead high-stakes Data-as-a-Service programs end-to-end. This role involves owning the program's success, including P&L, quality, timelines, and margin. The TDM will translate technical requirements into executable programs, design training data programs with clear task definitions and evaluation criteria, develop and scale the Expert Contributor Network, build and harden workflows and quality systems, monitor performance with data-driven insights, and manage internal/external communications. The role requires ML intuition for training data and evaluation, comfort with data manipulation (SQL, JSON, Python), and a strong bias for action in ambiguous, fast-changing environments.