Snorkel AI
snorkel.ai
Actively hiring
MLOps
Machine Learning
AI Training Data & Annotation
View 25 jobs
Follow
Overview
News
Technologies
Salaries
Products
People
Growth
Offices
Jobs
Financials
Funding and backers
Total raised
$150M
14 rounds on record
Latest round
$100M
Series D · May 2025 · 16 mo ago
Team
183
US Form 5500 (plan participants) · Dec 2025 · +8% in a year
Hiring now
44
+12 posted in 30 days
Grants
$6.8M
11 grants · CORDIS
Backers
6
Blumberg Capital, Greylock
The money's age
Due for its next round
16 months since the last round · and hiring
Momentum
36/100
hiring for its size, a new city, in the news
Last sign of life
this month
a posting or a news item
The story so fareach stage dated by its source: the company's releases, the press, registers, filings
Jul 2
On the Hacker News front page Senior SWE-Bench: open-source benchmark that assesses agents as senior engineers · 186 points
Feb 11
Launched on Hacker News Show HN: Open Benchmarks Grants– a $3M commitment to close the AI eval gap
Sep 19, 2025
Layoffs AI training unicorn Snorkel AI just laid off 13% of its workforce · Business Insider
May 29, 2025
Raised a round Series D $100M
May 22, 2025
Won a grant $1.2M · CORDIS
Jun 26, 2024
Won a grant $1.9M · CORDIS
Dec 15, 2023
Won a grant $73k · CORDIS
Dec 13, 2023
Won a grant $108k · CORDIS
Sep 20, 2023
Won a grant $1.2M · CORDIS
Aug 10, 2023
Partnership Snorkel AI deepens partnership with Google Cloud to help enterprises unlock the potential of large language models · businesswire.com
May 3, 2023
Won a grant $74k · CORDIS
Nov 4, 2022
Won a grant $75k · CORDIS
Jul 21, 2022
Won a grant $749k · CORDIS
May 9, 2022
Won a grant $1.2M · CORDIS
Nov 5, 2021
Won a grant $50k · CORDIS
Apr 16, 2021
Won a grant $50k · CORDIS
Apr 7, 2021
Raised a round Series B $35M · TechCrunch
Jul 17, 2020
Raised a round Venture round $15M · Pulse 2.0
2019
Founded
Tractionpublic signals, read monthly
Web presence
top 0.02%
of 133.2M domains by links, Common Crawl
Funding rounds
| Round | Date | Amount | Investors | Source |
|---|---|---|---|---|
| Series D | May 2025 | $100M | — | 01net.it |
| Grant | May 2025 | $1.2M | — | CORDIS |
| Grant | Jun 2024 | $1.9M | — | CORDIS |
| Grant | Dec 2023 | $73k | — | CORDIS |
| Grant | Dec 2023 | $108k | — | CORDIS |
| Grant | Sep 2023 | $1.2M | — | CORDIS |
| Grant | May 2023 | $74k | — | CORDIS |
| Grant | Nov 2022 | $75k | — | CORDIS |
| Grant | Jul 2022 | $749k | — | CORDIS |
| Grant | May 2022 | $1.2M | — | CORDIS |
| Grant | Nov 2021 | $50k | — | CORDIS |
| Grant | Apr 2021 | $50k | — | CORDIS |
| Series B | Apr 2021 | $35M | — | TechCrunch |
| Venture round | Jul 2020 | $15M | — | Pulse 2.0 |
Backed byfrom the funds' portfolios and accelerator catalogs
Blumberg Capital 
Greylock 
GV 
Lightspeed Venture Partners 
Shield Capital 
SV Angel Investors in similar startupsfunds that back the companies most like this one
Similar startupsby what they do, their industry and backers
Kili Technology is a French company founded in 2018 that provides a training data platform for annotation and quality control. Its tools…
Superb AI is a company founded in 2018 that provides a platform for building and managing computer vision training data. Its tools automate…
Datacurve produces high-quality training data for frontier models, focusing on software engineering and long-horizon reasoning tasks…
Refuel labels and cleans training data using large language models with human review where confidence is low. Teams describe the labelling…
V7 is a London company founded in 2018 that builds training data and workflow automation software for artificial intelligence teams. Its…
Labelbox is a San Francisco company founded in 2018 that provides a data engine for training and evaluating machine learning models. Its…
How Snorkel AI hires
Truth index
A 92/100
Median time to fill
102 days
Ghost roles
0%
Stale roles
11%
Reposted roles
0%
Snorkel AI scores A on the Alion truth index (A is best, E worst), measured over 18 open roles re-checked against its own hiring board: 0% look like ghost listings, 11% have gone stale and 0% are the same role posted again. Its roles close in a median of 102 days. It measures how much of what this employer publishes turns into a real, finite search - not the employer as a workplace. How the index is computed
Overview
Snorkel AI was founded in 2019 by Stanford researchers who developed programmatic labelling, where subject matter experts write rules that generate training data instead of annotating examples by hand. Its platform now covers data curation, model fine-tuning and expert evaluation for enterprise and frontier model builders. Banks, insurers and government agencies use it to specialise models on proprietary knowledge.
News
From Foundational Competency to Expert Performance: A Curriculum Approach to Model Development
A student does not go from 1st to 12th grade in a single step. Each grade builds on a specific set of skills, and each one assumes the previous skills have already been mastered. Nobody learns calculus without algebra. When a student skips ahead anyway, w
Read more
Report
OSWorld 2.0: Why Long-Horizon Computer-Use Agents Still Fail Four Out of Five Tasks
At our latest Snorkel AI Reading Group, held virtually for the first time, Mengqi Yuan (XLANG Lab, University of Hong Kong) presented OSWorld 2.0, a benchmark of 108 long-horizon, real-world computer-use workflows spanning 31 self-hosted websites and prof
Read more
Report
Fable 5.1 on Frontier Coding Tasks: Efficient Successes, Distinct Failure Modes
We evaluated Fable 5.1 on a series of frontier coding tasks from our proprietary Terminal-Bench+ dataset and compared the results against Opus 5. Fable remained competitive across most categories and was materially more efficient on successful runs, while
Read more
Report
Terminal-Bench 4.0: Why Continuous Benchmarks Require Continuous QA
The speed of new frontier model releases keeps accelerating. Meanwhile benchmarks struggle to keep up and saturate quickly, often being left in the dust. Most benchmarks are static datasets with no active maintenance, causing them to lose value fast. Some
Read more
Report
Why Frontier Agents Fail Real Engineering Work: Two Terminal-Bench 3.0 Task Deep Dives
Terminal-Bench 3.0 (formerly Frontier-Bench) recently launched, built to track what AI agents can and can't do across real computer work. Terminal-Bench 2.1 has been saturating, with top agents reaching 84%; on Terminal-Bench 3.0, the best model, Claude O
Read more
Report
Why Frontier Agents Fail Real Engineering Work: Two Terminal-Bench 3.0 Task Deep Dives
Terminal-Bench 3.0 (formerly Frontier-Bench) recently launched, built to track what AI agents can and can't do across real computer work. Terminal-Bench 2.1 has been saturating, with top agents reaching 84%; on Terminal-Bench 3.0, the best model, Claude O
Read more
Report
Continual Learning Bench: measuring whether AI systems actually improve with experience
At our latest Snorkel AI Reading Group, Parth Asawa (UC Berkeley Sky Computing Lab) presented Continual Learning Bench, the first expert-validated benchmark designed to measure whether LLM-based systems genuinely improve through sequential experience. CL-
Read more
Report
Train-to-Test (T²) Scaling Laws: Why Reasoning Models Should Be Overtrained
At our latest Snorkel AI Reading Group, Nicholas Roberts presented Test-Time Scaling Makes Overtraining Compute-Optimal, work coauthored with Sungjun Cho, Zhiqi Gao, Tzu-Heng Huang, Albert Wu, Gabriel Orlanski, Avi Trost, Kelly Buchanan, Aws Albarghouthi,
Read more
Report
Milestone-Based Evaluation and Training for Long-Horizon AI Agents
Long-horizon agents operate across many dependent states and transitions, often spanning multiple tools, environments, and periods of external feedback. The difficulty comes from preserving coherent progress as earlier decisions constrain later actions. A
Read more
Report
Enterprise environments and training AI agents for real-world workflows
Most agent benchmarks still evaluate a thin slice of the job. The agent receives a task, produces an answer, gets scored, and the episode ends. Enterprise workflows work differently. An underwriting agent may need to read policy documents, inspect custome
Read more
Report
Show more news
Tech stack
Technologies named in job postings at Snorkel AI over the last 12 months, by layer, with the share of postings for each.
Technologies in use
112
Postings analysed
59
Stack modernity
86/100AI adopter
Languages
Python36%
SQL8%
TypeScript5%
Node JS3%
Frameworks & libraries
Ray8%
Scikit-learn8%
PyTorch7%
LangChain5%
Databases
Chroma5%
Weaviate5%
Amazon Redshift2%
Snowflake2%
Cloud & platforms
AWS12%
Azure7%
GCP7%
Spark7%
AI & ML
LLM39%
Claude2%
LoRA2%
PEFT2%
Sign in to see how your skills match this stack
Sign In
Full Snorkel AI tech stack
112 technologies · by team · pay by technology · changes · similar stacks
Salary insights
Compensation ranges based on this company's open jobs
Median
$300k
Typical range
$180k – $444k
Open positions
38
33
Jobs
Middle
9.1%
Senior
33.3%
Staff
27.3%
Principal
9.1%
Architect
21.2%
| Level | Jobs | Median | Typical range |
|---|---|---|---|
| Middle | 3 | $300k | $185k – $300k |
| Senior | 11 | $220k | $180k – $320k |
| Staff | 9 | $320k | $300k – $444k |
| Principal | 3 | $280k | $240k – $330k |
| Architect | 7 | $315k | $290k – $425k |
Highest paying role
Strategic Delivery Lead
$444k
Normalized to USD per year
All salaries at Snorkel AI
By role, level and city, against the market · 38 jobs with pay
Growth
Hiring Momentum
76/100
Scaling fast
Open positions
44
12 opened / 8 closed in 30 days
Median time-to-fill
91 days
faster than 15% of the market
ATS activity
Every ~4 hours
Today
Hiring Dynamics
+100%
Hiring Focus
The percentage next to each role is its share of the company's job openings over the last 90 days; the arrow shows the shift versus the previous period.
Backend
16% ▼
Leadership
11% ▲
HR
11% ▼
AI/ML
8% ▲
Finance
8% ▲
Legal
8% ▼
Actively hiring for AI/ML (+3 in 30 days)
Activity Timeline
New location: Palo Alto
Oct 2026
New location: Redwood City
Oct 2026
New location: United States
Oct 2026
Added Outlook to stack
Oct 2026
Added Microsoft Excel to stack
Oct 2026
Added Rust to stack
Oct 2026
Added C++ to stack
Oct 2026
Added LLM Evaluation to stack
Oct 2026
Added Platform Engineering to stack
Oct 2026
Added Figma to stack
Oct 2026
Added Tableau to stack
Sep 2026
Added Looker to stack
Sep 2026
Offices
Where the company hires and what each office is for
Cities
4
Hiring now
4
Countries
1
Busiest office
Redwood City
Loading the map
Headquarters
Builds here
Office with open vacancies
Tap a pin for the office
Redwood City
United States
HQ
1 open roles 100% remote 1-10 est. staff $300k Avg Salary
hires for Solutions
San Francisco
United States
Engineering / R&D
San Francisco
42 open roles 31 posted in 90 days 29% engineering 12% remote 200-500 est. staff $299k Avg Salary
hires for Solutions, AI/ML, Backend
101 Second Street, San Francisco, CA, 94105
New York
United States
Engineering / R&D
New York City
33 open roles 26 posted in 90 days 30% engineering 12% remote 200-500 est. staff $307k Avg Salary
hires for Solutions, AI/ML, Backend
1178 Broadway, New York, NY 10001
Palo Alto
United States
Engineering / R&D
Remote
9 open roles 4 posted in 90 days 56% engineering 44% remote 50-200 est. staff $358k Avg Salary
hires for AI/ML, Solutions, Leadership
Jobs
$225k – $280k per year • Hybrid • 8+ years exp • New York • San Francisco
AI Agents
Snorkel
Design
Figma
Apply
≈ $246k – $448k per year (Estimated) • Hybrid • 5+ years exp • New York • San Francisco
Python
Dagster
Prefect
Reinforcement Learning
AI Agents
LLM
Ray
Synthetic Data
Snorkel
Agentic Workflows
Multi-Agent Systems
DevOps
AWS
Kubernetes
Apply
$230k – $360k per year • Remote (United States) • 8+ years exp • New York • San Francisco • Palo Alto
Python
FastAPI
Mypy
Databases
Weaviate
Chroma
FAISS
AI/ML
LangGraph
LangChain
Spark
LlamaIndex
Fine-tuning
Scikit-learn
Prompt Engineering
AI Agents
Transformers
PyTorch
CrewAI
LLM
RAG
Ray
Hugging Face
Snorkel
Agentic Workflows
Machine Learning
QA
Pytest
Apply
$180k – $300k per year • Equity • Remote (United States) • 4+ years exp • Bachelor's Degree • New York • San Francisco • Palo Alto
Python
RLHF
Multimodal AI
AI Agents
LLM
Synthetic Data
DPO
Post-training
Snorkel
LLM Evaluation
RLAIF
Machine Learning
Apply
$200k – $375k per year • Hybrid • PhD • New York • San Francisco • Palo Alto
Reinforcement Learning
AI Agents
NLP
Synthetic Data
Snorkel
Human-in-the-Loop
Computer Use
Machine Learning
Apply
≈ $239k – $479k per year (Estimated) • Hybrid • 10+ years exp • New York • San Francisco
AI Agents
LLM
Snorkel
DevOps
Platform Engineering
Apply
$200k – $375k per year • Hybrid • PhD • New York • San Francisco • Palo Alto
Python
Scikit-learn
Multimodal AI
AI Agents
NLP
TensorFlow
Pandas
NumPy
PyTorch
LLM
Synthetic Data
TPU
Snorkel
Machine Learning
DevOps
GCP
AWS
Web3
Rollup
Apply
$150k – $220k per year • Hybrid • 4+ years exp • New York • San Francisco
Python
JavaScript
PowerShell
Node JS
Claude
Model Context Protocol
Prompt Engineering
AI Agents
LLM
OpenAI
Anthropic
Snorkel
Machine Learning
Frontend
GraphQL
DevOps
Rest API
Terraform
Azure
CI/CD
Windows
TCP/IP
VPN
Wi-Fi
Cybersecurity
Okta
Crowdstrike
ISO 27001
SOC 2
Zero Trust
Least Privilege
Auth0
SIEM
Management
Slack
Notion
Jira
Google Workspace
Zapier
Apply
$275k – $425k per year • Hybrid • New York • San Francisco • Palo Alto
AI Agents
LLM
Snorkel
Apply
$200k – $375k per year • Hybrid • PhD • New York • San Francisco • Palo Alto
AI Agents
NLP
LLM
Snorkel
Machine Learning
Apply
$252k – $370k per year • Hybrid • Contractor • 10+ years exp • Bachelor's Degree • San Francisco • New York
AI Agents
Snorkel
DevOps
CI/CD
AWS
IAM
Cybersecurity
ISO 27001
SOC 2
Threat Modeling
SIEM
Apply
$200k – $310k per year • Equity • Hybrid • 8+ years exp • New York • San Francisco
AI Agents
Snorkel
Apply
Financials
Total raised
$156.8M
Latest round
Series D
Funding rounds
14
Funding rounds
| Round | Announced | Amount | |
|---|---|---|---|
| Series D | 2025 | $100M | Details |
| Grant | 2025 | $1.2M | Details |
| Grant | 2024 | $1.9M | Details |
| Grant | 2023 | $73k | Details |
| Grant | 2023 | $108k | Details |
| Grant | 2023 | $1.2M | Details |
| Grant | 2023 | $74k | Details |
| Grant | 2022 | $75k | Details |
| Grant | 2022 | $749k | Details |
| Grant | 2022 | $1.2M | Details |
| Grant | 2021 | $50k | Details |
| Grant | 2021 | $50k | Details |
| Series B | 2021 | $35M | Details |
| Venture - Series Unknown | 2020 | $15M | Details |