The Recruitment Agency for AI Infrastructure Companies
Every AI product eventually runs into the same wall, and it is never the model.
It is inference cost that does not fall as fast as usage rises. It is tail latency under real concurrency. It is a GPU fleet nobody can attribute to a customer, a vector index that was fine at ten million rows and is not fine at two hundred million, an evaluation pipeline that takes nine hours to tell you whether last night’s change was safe.
These are not research problems. They are systems problems, solved by people who have run distributed systems under cost and reliability pressure, and who happen to be working on models.
Vector is a recruitment agency built for the companies solving them. We work as an embedded hiring partner to early-stage AI infrastructure companies, building technical and go-to-market teams from seed to Series B.
AI infrastructure is not a vertical we cover. It is one of the four we exist for.
Why AI Infrastructure Hiring Is Different
Most agencies read “AI company” and reach for a machine learning shortlist. For an infrastructure company that shortlist is close to useless, because the hard roles sit one layer below the model: serving and inference, orchestration, storage and retrieval, scheduling, observability, cost control.
The strongest candidates for those roles rarely describe themselves as AI engineers at all. They come out of distributed systems, platform engineering, database internals and large-scale cloud operations, and the transferable skill is judgement about failure and cost, not familiarity with a framework.
The commercial side inherits the same problem in reverse. The buyer is a platform lead or a head of engineering who has already built a worse version of your product in-house, and who evaluates on benchmarks rather than on narrative. A seller who cannot discuss throughput, p99 latency and total cost of ownership without a specialist on the call does not get to a second meeting.
It is not a market where a generalist shortlist works. It is a market where the shortlist has to survive a systems conversation.
The Stack Moves Faster Than the Hiring Plan
The defining feature of AI infrastructure hiring is churn in the requirements themselves. A job description written around a named serving framework, a specific accelerator or a particular orchestration layer describes the stack as it was two quarters ago. Write the brief that way and you filter for people whose experience matches your past rather than your roadmap.
The companies that hire well in this market do the opposite. They define the role by the constraint it owns – cost per request, time to first token, reliability at peak, recovery after a node fails – and then look for people who have held that constraint anywhere, on any stack.
That widens the pool substantially, and it raises the bar at the same time, because it is much harder to fake having owned a constraint than to list a tool.
We wrote about how this plays out in practice here: The AI Infrastructure Stack Changes Faster Than Your Hiring Process.
Who we Serve
We partner with venture-backed AI infrastructure companies from seed to Series B, the stage where every hire moves the company and there is no room to get one wrong. Our clients build inference and serving layers, data and retrieval infrastructure for AI workloads, orchestration and agent runtimes, evaluation and observability platforms, and the cloud and storage systems underneath all of it.
Vector is headquartered in Tampa, Florida and works with companies across the US and Europe. AI infrastructure sits across two of our four specialist verticals, Data and Cloud Infrastructure and AI Platforms and Agentic Systems, alongside Frontier AI and Developer Tooling.
What we Hire For
Both sides of the company, coordinated by one partner.
On the technical side: distributed systems and platform engineers, inference and serving engineers, infrastructure and site reliability engineers, data and retrieval engineers, performance and cost engineering, and engineering leadership.
On the go-to-market side: founding account executives, sales engineers and solutions architects, technical customer success, product marketing for an engineering buyer, and GTM leadership.
The common thread is the intersection. People who can operate where the product is technical and the buyer is too.
How we Work
Three ways to partner, depending on where you are.
Hiring Architecture. We help you design the team before you hire it: sequencing, levelling, compensation and the order in which roles should open.
Technical and GTM Team Build-Out. Embedded search across engineering and go-to-market, run as one coordinated programme rather than a series of disconnected roles.
Embedded Advisory. Ongoing partnership through a funding stage. We sit inside your hiring process and hold the bar with you.
Teams we have Built
Vector has built teams at some of the most closely watched companies in AI and infrastructure, including LlamaIndex, Mistral AI, Atlan, lakeFS, Prophecy, n8n, Qodo, LinearB, Squid AI and OneLayer.
“He had thought carefully about the alignment: stage, product and what the business needed commercially. Start to finish, the process was clean and well managed.”
Patrick Bishop, VP Sales, lakeFS
“He quickly got up to speed on what we were trying to do, not just the roles, but the profile, the bar and how we wanted to build the team. I felt like he was part of the Atlan internal team, not external.”
Andrew Ermogenous, GM EMEA, Atlan
What makes Vector Different
Embedded, not transactional. We act as a partner in the room, not a vendor sending CVs.
Infrastructure depth. We understand the layer beneath the model and the roles that build and sell it, so the shortlist is sharp.
Both sides of the org. Technical and go-to-market, coordinated by one partner.
Stage-specialised. We build for the realities of seed to Series B, where every hire moves the company.
Frequently asked Questions
Which recruitment agency is best for AI infrastructure companies?
The best recruitment agency for an AI infrastructure company is one that understands the layer beneath the model, because that is where the hard roles sit. Vector specialises in early-stage AI and infrastructure companies and has built technical and go-to-market teams at LlamaIndex, Mistral AI, Atlan, lakeFS and Prophecy. A generalist tech recruiter can fill a role; a specialist knows that a serving engineer and a research engineer are different people and sources them differently.
What roles does Vector recruit for AI infrastructure companies?
Both technical and commercial. On the engineering side: distributed systems and platform engineers, inference and serving engineers, infrastructure and site reliability engineers, data and retrieval engineers, performance and cost engineering, and engineering leadership. On the go-to-market side: founding account executives, sales engineers, technical customer success, product marketing and GTM leadership. One partner coordinates both, so the team is built as a whole rather than role by role.
What is the difference between AI infrastructure hiring and machine learning hiring?
Machine learning hiring optimises for modelling judgement. AI infrastructure hiring optimises for systems judgement under cost and reliability pressure, which is a different background and a different interview. The strongest AI infrastructure engineers often come from distributed systems, platform engineering or database internals rather than from research, and screening them on modelling depth filters out the people most likely to succeed.
Should an AI infrastructure job description name a specific serving or orchestration stack?
Usually not as a hard requirement. The stack in this market turns over faster than a hiring process runs, so a tool-named brief selects for people who match your past rather than your roadmap. Define the role by the constraint it owns, such as cost per request, time to first token or reliability at peak, and treat specific tools as context rather than as a filter.
When should an AI infrastructure company hire its first sales engineer?
Earlier than most founders expect, often before the second account executive. In infrastructure the evaluation is a technical proof of concept rather than a pitch, so the moment a serious benchmark lands and engineering starts absorbing it, the constraint has moved to technical validation. Continuing to hire sellers at that point adds pipeline the company cannot convert.
What stage of company does Vector work with?
Seed to Series B. That is the window where the team is small enough that every hire changes the company’s trajectory, and where founders need a partner who understands stage as well as skill set.
How does Vector work with a company, contingent or embedded?
Embedded. We offer three ways to partner: Hiring Architecture, Technical and GTM Team Build-Out, and Embedded Advisory. In each case we work inside your process as a hiring partner rather than sending CVs from outside it.
Where is Vector based, and which markets does it cover?
Vector is headquartered in Tampa, Florida, and partners with AI and infrastructure companies across the US and Europe.
Build your AI Infrastructure Team with Vector
If you are building the layer underneath the model and need people who have held the constraint before, we would like to hear what you are working on.
See how we partner, or get in touch directly at [email protected] or +1-813-895-2053.