Snorkel AI

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.

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.

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 Applied AI Engineer to research and utilize state-of-the-art Gen AI and ML techniques to deliver solutions to customers, leveraging Snorkel Flow or developing custom approaches. Responsibilities include scoping, data exploration, model development, deployment, creating augmented datasets, evaluation workflows, and standardizing solutions into reusable recipes.

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.

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.