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WHIRL AI
AI agents for enterprise IT operations, the foundational context layer that makes Enterprise AI actually work inside complex organisations
Last verified August 17, 2026 · Updated daily
What WHIRL AI is building
Enterprise AI keeps stalling for a specific, well-documented reason: the AI has no idea how your systems actually work. Every large enterprise runs on a tangle of customised ERP configurations, undocumented API integrations, workarounds built by people who left three years ago, and business logic that exists nowhere except in institutional memory. When you ask an AI agent to change a core business process inside that environment, it is operating blind. The result is enterprise AI stuck in pilot phase permanently. Whirl AI is building the context layer that fixes this. The platform continuously ingests metadata from enterprise systems and converts it into a living, searchable knowledge base, maintaining real-time system intelligence that updates as environments change: which applications exist, how they are configured in practice, what integrations depend on what, what the actual business processes are versus what the documentation says. Purpose-built AI agents then operate on top of that context to help IT teams research, design, develop, implement, and test changes to applications and integrations, compressing work that previously took weeks into hours.
Why this matters
ICONIQ is primarily a growth-stage fund. They have backed Snowflake, Figma, Workday, and ServiceNow. Leading a seed round is so unusual for ICONIQ that they made it their headline: this is described as "one of ICONIQ's earliest ever investments." The partner who led it, Matt Jacobson, worked alongside Sunny Bedi directly at Snowflake and had a front-row view of the exact operational problem Whirl is solving. His public statement is unusually specific: "The problem Whirl solves is not theoretical to him. He lived it firsthand, at scale, for two decades." The angel roster from Okta, Splunk, and VMware represents three of the most consequential enterprise software companies of the last twenty years. These are people who built and sold to enterprise IT at scale and understand the problem firsthand. The competitive context is also instructive: Glean raised a $150M Series F at a $7.2B valuation in June 2025, and Moveworks was acquired by ServiceNow in March 2025. The market for enterprise AI context and intelligence layers is validating rapidly, and Whirl is at the earliest possible entry point.
Open roles at WHIRL AI
3 positions we're tracking. Roles are re-checked daily and removed when filled.
Know when WHIRL AI is hiring before anyone else
A role stays uncontested for about four days. Here's the window — and where we put you in it.
From $9/month, cancel any time.
Watching WHIRL AI
0 applicantsRole spotted & verified
1You get the alert
1You've applied
~8Hits the job boards
250+Hiring outlook
Very High. ICONIQ leading their first-ever seed investment is the most distinctive signal on this list. Matt Jacobson worked alongside Sunny at Snowflake personally. The company page explicitly states "Production AI experience. Enterprise standards. Startup speed. If that sounds like you, we are hiring."
Working at WHIRL AI
WHIRL AI is AI agents for enterprise IT operations, the foundational context layer that makes Enterprise AI actually work inside complex organisations, founded in and now <50 people. What this means for you: broad remit, direct access to founders, and equity that still means something if the company works out.
San Francisco is where most WHIRL AI positions are located.
How to actually get hired at WHIRL AI
Why applying the normal way doesn't work
With only <50 employees, WHIRL AI doesn't have dedicated recruiters. Founders review applications between running the company. The challenge isn't competition — it's visibility. Direct outreach wins.
Who to contact at WHIRL AI
Founding team
Sunny's career is one of the cleanest examples of founder-market fit in enterprise AI right now. He holds a BS and MBA from the University of San Francisco, and completed Executive Education in Leadership and Technology at Stanford. His career began in consulting at Andersen Consulting and Deloitte, where he built the foundational understanding of enterprise system complexity that defines his entire professional perspective. He then moved through a sequence of corporate IT leadership roles that trace the arc of enterprise technology itself: VMware, where he built operational infrastructure during one of the most consequential periods in enterprise virtualisation; JDSU, a telecom equipment manufacturer with highly complex operational systems; and then NVIDIA from 2008 to 2020, where he joined when the company had fewer than 2,000 employees and left when it had over 15,000, building and scaling the IT and operations infrastructure that supported the company's transformation from gaming GPU maker into the world's most strategically important AI hardware company. That twelve-year tenure at NVIDIA is the central credential. Scaling IT infrastructure from 2,000 to 15,000 employees across the period when NVIDIA was becoming NVIDIA is not a typical enterprise IT experience. He then joined Snowflake in January 2020 as CIO and Chief Digital Officer, where he became a public advocate for "Snowflake on Snowflake", the philosophy of using Snowflake's own products internally to demonstrate their value to customers. ICONIQ partner Matt Jacobson watched him operate in this role firsthand, which is why ICONIQ's first seed investment is in Whirl. His public writing on LinkedIn consistently returns to the same theme: "Every CIO I know wants to use AI to make IT more responsive and transformational. But it keeps stalling." He launched Whirl specifically because he believes no one else was solving the right underlying problem.
Marco's background is in enterprise GTM strategy at DocuSign, where he held the role of VP of GTM Strategy and scaled the operation significantly during a period of rapid growth. The company page describes him as a "GTM leader who scaled DocuSign 8X and lived the same problem from the business side," which is a precise framing: DocuSign's agreement management platform operates inside enterprise systems and faces the same context and integration complexity that Whirl is solving, but from the customer side rather than the IT side. Marco's decade-plus at DocuSign, across strategy and operations, gives Whirl's commercial function the enterprise GTM muscle that complements Sunny's product and market insight. He studied at the Tuck School of Business at Dartmouth.
What to show them
Whirl's core technical challenge is maintaining a continuously updated, structured knowledge graph of enterprise system context across applications, integrations, and configurations. The AI agents that operate on top of that context need to be able to reason across it reliably, handle ambiguity when documentation is incomplete, and surface changes when environments evolve. Building that layer at the reliability standard enterprise IT demands is a hard engineering problem. Core skills: Python, knowledge graph architecture or graph databases (Neo4j or similar), LLM orchestration for enterprise tool integration, ERP/CRM system integration (Salesforce, SAP, ServiceNow), structured metadata extraction, enterprise security standards, agentic AI systems. Proof of work: Map the technical architecture for a system that could continuously ingest metadata from Salesforce and ServiceNow simultaneously, identify the integration dependencies between them, and surface a change log when a new workflow is created in either system. Even a one-page design doc shows you understand the system intelligence problem Whirl is solving before the conversation starts.
A cold email that works at WHIRL AI
What WHIRL AI screens for
The founding team is already ~12 people and productively deployed with design partners. The company page is explicit: "Production AI experience. Enterprise standards. Startup speed." The team composition as listed on the company page reveals the functional shape of the hiring plan. Security and operations are already staffed at the founding level (Mario Duarte on security, Marco Castillo on operations, multiple engineers, product members). The gaps that will open next are the commercial and AI-product layers: enterprise sales, customer success for complex IT environments, and AI engineers who can extend the agent capabilities.
Tailor your CV to the specific WHIRL AI role rather than sending a general one. Applications that mirror the language of the job description clear automated filters at a materially higher rate.
Mistakes that kill WHIRL AI applications
Don't send the same CV you sent everywhere else. At <50 people it's obvious, and it's the fastest rejection there is.
Lead with their problem, not your ambition. WHIRL AI is focused on ICONIQ is primarily a growth-stage fund — show you understand that.
Most AI applications get ghosted. A day-five follow-up can double your response rate.
Applying to WHIRL AI? Get the contact, not the form.
The WHIRL AI interview process
3 stages · 7 days typical · take-home: yes · modelled from similar companies
We don't yet have verified candidate reports for WHIRL AI. What follows is the typical process for a <50-person AI company — treat it as a model, not confirmed detail.
Interview stages
Intro Call
Video call · 30 min
Culture fit and role expectations
Founder or hiring manager
Technical Deep Dive
Video call or in-person · 60 min
Past projects and problem-solving approach
Technical founder or lead
Final Round
In-person or video · 45 min
Team fit and offer discussion
Founding team
WHIRL AI take-home assignment
WHIRL AI includes a take-home exercise in their interview process. For AI roles, this typically involves a practical problem that takes 2-4 hours. Focus on clean, working code over premature optimization. They're evaluating how you think and communicate, not just the solution.
WHIRL AI interview timeline
WHIRL AI runs about days from first contact to offer. The median for AI companies at <50 people is 10 days, so WHIRL AI is faster than most.
Interviewed at WHIRL AI?
Tell us how it went — stages, questions, timeline. Takes 90 seconds and it's how this page stays accurate for the next person.
Submit your WHIRL AI interview experience →WHIRL AI jobs, frequently asked questions
How many jobs does WHIRL AI have open?
We're tracking 3 active openings at WHIRL AI (verified April 2026).
Does WHIRL AI hire remotely?
Currently, WHIRL AI only has in-office roles in San Francisco.
What roles is WHIRL AI hiring for?
WHIRL AI is hiring across Engineering, Sales, Customer. The most recent opening is Senior AI / Platform Engineer.
How do I apply for a job at WHIRL AI?
Click through to apply, or see our detailed guide on landing a job at WHIRL AI.
Does WHIRL AI respond to cold emails?
We haven't verified response rates at WHIRL AI yet.
Who is the hiring manager at WHIRL AI?
At this size, hiring is usually run by a founder or department head.
How competitive is it to get hired at WHIRL AI?
Roles at <50-person AI companies typically draw 50-100 applicants in the first two weeks. Applying inside 72 hours of a posting going live is the single biggest lever you control.
How many rounds is the WHIRL AI interview?
3 stages: Intro Call, Technical Deep Dive, Final Round.
Is the WHIRL AI interview hard?
The interview emphasizes technical depth and system design over abstract problems. Hardest stage: Technical Interview.
Does WHIRL AI give a take-home task?
Yes, WHIRL AI includes a take-home assignment.
How long does WHIRL AI take to get back to you?
Around 7 days across the full process.
What should I prepare for the WHIRL AI interview?
Prepare for technical depth and system design. A <50-person startup wants proof you can ship, not that you can whiteboard.
Where is WHIRL AI based?
WHIRL AI is headquartered in San Francisco, US.
Get WHIRL AI roles before they're posted
A role stays uncontested for about four days. Here's the window — and where we put you in it.
From $9/month, cancel any time.
Watching WHIRL AI
0 applicantsRole spotted & verified
1You get the alert
1You've applied
~8Hits the job boards
250+