Snorkel AI Tech Stack

Data infrastructure for building specialized AI models

Software Development Redwood City, California 51–200 employees Founded 2019 Privately Held

Snorkel AI builds the data layer for enterprises and government agencies developing custom AI systems. The stack is heavily weighted toward ML infrastructure (PyTorch, TensorFlow, Kubernetes, Slurm) with emerging investment in GPU cluster infrastructure and experiment tracking—revealing a shift toward in-house model training at scale. Hiring velocity is accelerating across ops and engineering, while pain points cluster around data delivery bottlenecks and generation efficiency, suggesting the product roadmap is addressing foundational data-pipeline constraints that customers hit at prototype-to-production transitions.

Tech Stack 30 technologies

Core StackPython NumPy scikit-learn Pandas PyTorch TensorFlow AWS Tableau Power BI Looker Salesforce Zapier Airtable Make Kubernetes Gong n8n GCP TPU SQL JSON Google Workspace Excel Google Sheets Tilt Notion Slurm Outreach Apollo Claude

What Snorkel AI Is Building

◆Challenges

Scaling data delivery
Reducing concentration risk
Operational bottlenecks
Unblocking projects
Ai data bottlenecks
Data hygiene
Improving data generation efficiency
Gaps in contributor funnel
Reducing ramp times
Improving win rates

▲Active Projects

Synthetic data generation
Automated form workflow
Snorkel flow next generation ai tooling
Internal gtm data governance tool
Knowledge base in notion
Centralized learning hubs
Competency-based onboarding program
Quality estimation
Experiment tracking
Gpu cluster infrastructure

Hiring Activity

Accelerating110 roles · 50 in 30d

Department

Ops

Engineering

Data

Sales

Marketing

Operations

Product

Research

Seniority

Senior

Manager

Mid

Lead

Principal

Staff

Director

Junior

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About Snorkel AI

Founded in 2019 from Stanford AI Lab research, Snorkel AI provides programmatic data development technology for organizations building domain-specific AI systems. The product targets frontier labs, enterprises, and government agencies that need to generate, label, and curate high-quality training data at scale. The company operates across the United States, Mexico, and the United Arab Emirates. Current project focus spans synthetic data generation, quality estimation, and workflow automation, paired with internal emphasis on data governance tooling and contributor onboarding—indicating both customer-facing product development and organizational scaling pressure.

HeadquartersRedwood City, California

Company Size51–200 employees

Founded2019

Hiring MarketsUnited States, Mexico, United Arab Emirates

Frequently Asked Questions

What tech stack does Snorkel AI use?

Core stack: Python, PyTorch, TensorFlow, NumPy, Pandas, scikit-learn. Infrastructure: AWS, GCP, Kubernetes, Slurm, TPU. Analytics/BI: Tableau, Power BI, Looker. Recent additions: GPU cluster infrastructure and experiment tracking systems.

What is Snorkel AI working on?

Product focus: synthetic data generation, quality estimation, and snorkel flow (next-generation AI tooling). Internal projects: GPU cluster infrastructure, experiment tracking, data governance tools, and competency-based onboarding.