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← Companies
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VAST Data
AI Infrastructure / Data Platform1000+Tel Aviv, Israel / New York, USAFounded 2016
About VAST Data
VAST Data builds an AI data platform that unifies storage, database and compute services for data-intensive infrastructure.
People & hiring
Company scale, with the denominator visible
Employee figures and vacancy signals describe different things. Each chart names its scope and observation period.
No sourced employee headcount or technical-staff share is available for this company yet. Vacancy counts below are never used as employee estimates.
ApplyDjinn hiring evidence · current 30-day window
Explore these jobs →Current open roles
22+2
Change versus 2026-10-28.
Technical open roles
1359%
Classified from the same current vacancies. This is a hiring mix, not the employee mix.
Live vacancy evidence
Technology coverage at VAST Data
Coverage always uses all 22 active roles as the denominator. Scope only changes which requirement signals count.
- Active roles
- 22
- Technologies
- 34
- Hard mentions
- 94
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- 17%
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BGP
5 of 22 roles
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%22%2F%3E%3Ctext%20x%3D%2248%22%20y%3D%2250%22%20fill%3D%22%23ffffff%22%20font-family%3D%22ui-sans-serif%2Csystem-ui%2CArial%2Csans-serif%22%20font-size%3D%2246%22%20font-weight%3D%22600%22%20text-anchor%3D%22middle%22%20dominant-baseline%3D%22central%22%3EFI%3C%2Ftext%3E%3C%2Fsvg%3E){kind=small}
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Curated company stack
Company-level metadata, kept separate from live vacancy requirements.
C++PythonLinuxKubernetesDistributed SystemsCloudAI
Perks & Benefits
✓AI infrastructure
✓Global teams
✓Professional growth
✓Benefits
Company Details
Industry
AI Infrastructure / Data Platform
Company size
1000+
Headquarters
Tel Aviv, Israel / New York, USA
Founded
2016
Domain
vastdata.com
Rating
—
The company activity baseline is being collected.
Open roles
22
Technical roles
13 of 22
Observed hiring change
-11 roles
Hiring and market history
Daily vacancy snapshots show this company’s observed hiring; no standalone public ticker is assigned.
Open roles
Vacancy history
Current role mix
13 technical · 9 other
59% of currently observed roles are technical.
22 open positions at VAST Data
Showing the latest 0 here.
Employee perspectives
Reviews of VAST Data
Publisher ratings on a shared 5-point scale. The overall score averages the available publishers equally.
Glassdoor
Employee ratings and reviews
Open Glassdoor to check its current rating.
DOU
A verified DOU profile is not available for this company yet.
Dated snapshots may differ from the latest source. DOU scores are converted from 100 to 5 by dividing by 20. Survey responses and written reviews are separate counts. Review text stays on the publisher’s site. Glassdoor may require sign-in.
Company monitor
Published about VAST Data
Official updates and independent stories that name VAST Data, with dates and source attribution. Each preview opens here first.
8 verified updatesOfficial feed monitored
View news and attention signals for VAST Data →Official · vastdata.com
CoreWeave on Building Resilient AI Infrastructure at Massive Scale
CoreWeave's Jacob Yundt on why the datacenter itself is becoming the computer for AI infrastructure at massive scale.
Open story →Official · vastdata.com
AI
Kafka Spark AI VAST DASE
Open story →Official · vastdata.com
Building Semantic Enterprise Search for AI Agents
Rakshith Venkatesh, Director of Product Management; Calvin Nieh, Technical Alliances Product Marketing Manager; and Derrick Harris, Technology Storyteller With the agentic AI era kicking into high gear, data management is more important than ever. AI agents need more than just data, no matter how well it’s annotated or curated. What AI agents need is data that’s organized, contextual, and easy to parse. They need to know how data from different sources (both structured and unstructured) relate to each other and the broader world through a shared ontology. In short: AI agents need semantic data. Inside large organizations, they need enterprise search that not only works, but also is supercharged with the ability to choose the best sources to serve and then to reason over them. Working with NVIDIA Agent Knowledge Foundation, including libraries like NVIDIA Auto-Ontology, we’ve developed a framework for implementing these capabilities without the heavy manual effort it previously would have required. By combining theVAST AI OS with NVIDIA’s growing collection of open models, accelerated libraries, and data-processing technologies, we’re able to automate an entire semantic workflow from data ingestion through 10x more accurate predictive results. All human experts need to do is ask their questions.
Open story →Official · vastdata.com
Powering Production AI with VAST, NVIDIA, and Agent Sandboxes
Sagi Grimberg, VP of Architecture | Simon Golan, Data Solutions Team Lead | Derrick Harris, Technology Storyteller Rather than rushing AI agents into production, large enterprises, tech companies, and other security-conscious organizations are keeping agents on a tight leash until they can get them under control. When we set AI agents free to solve a problem or complete a task without first determining every step they should take, we’re treating them like human employees. Unlike human employees, however, AI agents lack accountability. There’s no concern of repercussions if their actions negatively impact the business. All you can do is attempt to unwind any damage and ensure the same mistake doesn’t happen again. Also unlike humans, agents can proliferate as fast as we want them to. One agent today could become 1,000 agents tomorrow, all without HR lifting a finger.
Open story →Official · vastdata.com
The New AI Builder Stack Is Shrinking Development from Months to Hours
There is something pretty remarkable happening in AI development right now. Work that once required weeks of infrastructure setup, pipeline integration, and specialized engineering can increasingly be done in an afternoon. The interesting part is what’s making that possible. Coding agents are getting much of the attention, but the real acceleration comes when those agents have access to a complete, functioning data and inference stack. That’s the idea behind the VAST Builders Challenge, a series of hands-on, single-day events where developers build working video AI applications using Cursor, NVIDIA models, CoreWeave GPU infrastructure, and VAST AI OS. Rather than spending their time configuring storage, moving video datasets around, or wiring up inference pipelines, participants arrive with the heavy lifting already done. They can focus on building something useful. In this episode of Shared Everything, I talk with Brian Verkley, Director of AI Data Platform at VAST Data, and Ram Bansal, Senior Developer Advocate at VAST Data, about what happens when you remove those infrastructure barriers and give developers access to tools and datasets they might never have been able to work with otherwise.
Open story →Official · vastdata.com
Enforce HIPAA Compliance with the VAST AI Operating System
Howard Marks, VAST Technologist Extraordinary and Plenipotentiary The Health Insurance Portability and Accountability Act (HIPAA) requires organizations that hold health records and related personally identifiable information to keep that data private for extended periods. The ultimate goal is to provide a framework that, coupled with enforcement, ensures patient healthcare data is limited to those with a stated need to know. As technologists, it’s easy to view HIPAA compliance as a technology challenge. In reality, organizations can achieve compliance only through a combination of training, business policies, and technology. That said, the VAST AI Operating System provides the technology to enforce organizations' HIPAA compliance policies, as described below. The HIPAA security rules require organizations to “implement a mechanism to encrypt ePHI whenever deemed appropriate” to guard against unauthorized access to ePHI (electronic personal health information) transmitted over an electronic communications network.
Open story →Official · vastdata.com
From Moving Content to Moving Intelligence
Nicole Hemsoth Prickett, Head of Industry Relations | John Alexander, SVP, Cloud Product Management, Akamai | Ari Weil, VP, Product Marketing & Developer Relations, Akamai Akamai has been distributing infrastructure since 1998, long before “edge” became a category of computing and certainly before anyone was thinking about where an AI agent should run. The original problem was straightforward then. The internet could not scale if every request had to travel back to centralized infrastructure. Move content closer to users, route intelligently around constraints, and the network itself becomes part of the architecture. And now, nearly three decades later, AI inference is presenting a familiar problem, only this time the thing being distributed is intelligence.
Open story →Official · vastdata.com
Topics to Tables: From JSON on a Topic to Queryable Columns
In Part 1of this blog we described the basic model: point a Blob Expansion at a Kafka topic, declare the columns you need, and VAST parses each message into a typed table as the message is ingested. The expansion runs in the same transaction as the produce operation. Here, we’ll walk through the setup and the results. We will produce order records as JSON to a Kafka topic and query them back as typed columns. There is no consumer group or separate processing pipeline. We will then send malformed messages to the same topic and look at the diagnostic columns, followed by a schema change without recreating the expansion. The expansion converts JSON on the topic into columns in the target table as the message is ingested. The examples below use output from a release-5.5.0-sp1 cluster. The setup is small and does not require a separate streaming engine or consumer group. On the client side, we use the kafka-python and VAST DataBase Python packages. Blob Expansion is a first-class database object, managed through the GUI, CLI, REST API or Python SDK like the topic and target tables. Most of the setup here uses code and CLI commands, with GUI screenshots for the expansion configuration.
Open story →