Cloud is now one of the biggest business expenses and one of the hardest to manage.
At Finout, we’re not just shedding light on spend—we’re giving companies the power to make smarter, faster, and more strategic decisions about the cloud.
We’re trusted by brands like The New York Times, Wiz, Elastic, SiriusXM, and Lyft, and backed by top-tier investors with over $85M raised. In just 4 years, we’ve grown to 100+ people across Tel Aviv and New York—and we’re just getting started.
If you’re looking to build something big, solve real problems, and grow fast -we’d love to meet you.

You've been in the meeting. The cloud bill goes up on the screen, the number is bigger than last month, and nobody in the room can say why. Finance wants an explanation. Engineering can't read the bill. Everyone agrees to look into it. Nobody does.

That meeting is our product. The New York Times, Wiz, Elastic, SiriusXM and Lyft use Finout to find out where the money actually goes and what to do about it. $85M raised, 100+ people across Tel Aviv and New York, four years old, still shipping like it's month three.

Now it's happening again, faster. Your company went from spending nothing on AI to serious money in under two years, and when someone asked which team, which feature, which customer — the answer was a shrug. The unit of consumption is a token, and no accounting system on earth has a column for it. Cloud took fifteen years to break properly. AI is managing it in eighteen months, and every CFO funding it is already asking what they're getting. Nobody has answered yet.
About the role

You'll own Finout's enrichment layer — the proprietary part of the platform that joins operational metrics with cost data.

That sounds quiet until you see what it decides. A bill tells you what you spent. Metrics tell you what actually happened: which pod ran, how long a GPU sat idle, how many tokens a feature burned, whose request caused it. Join them and you can answer the only questions anyone truly cares about — what did this cost, who caused it, was it worth it. Keep them apart and you've got an invoice and a Grafana dashboard that never speak to each other.

Nobody else in this market has that layer. It's why customers pick us, and it's yours to push forward.

Kubernetes is the biggest use case. A cluster is a blender — dozens of teams, hundreds of services, thousands of pods sharing nodes, and one line on the invoice that says EC2. Enrichment is what turns that into cost per namespace, workload, team and customer. Then into efficiency: requests versus real usage, idle nodes, bin packing, spot and autoscaling, each one turned by CostGuard into a dollar figure and something to do about it. Then into planning: what this cluster costs next year when the workload doubles.

AI is the newest. The GPU nodes landing in those same clusters, and above them the AI gateway traffic that has to be enriched until a token lands on the team, the feature and the person who actually spent it. Nobody attributes this properly today, and no vendor ships savings recommendations based on real GPU utilization. Not one. Whoever builds it well sets how the industry measures AI efficiency.

And then whatever's next. A new provider's billing model, a telemetry source nobody has mapped, a customer's data at a scale that breaks the obvious approach. Those land with whoever can take them — nobody here owns a lane. The constant isn't the domain, it's the kind of problem: technical, data-heavy, unsolved.

One more thing worth knowing. This is a system where people start their morning inside. A FinOps lead opens Finout at 9am to find out what happened yesterday, and if the number is late, or wrong, or doesn't reconcile with the invoice, we failed — no matter how good the feature looked in review. If that stake appeals to you rather than frightens you, you'll fit here.

How we work, and who we're looking for

We move fast, we work hard, and we have a good time doing it. You'll love it here if:

• You prototype with AI, hand it to our coding agent, and get an answer in days instead of quarters. Most product people spend a career writing about the product — you'd rather make it

• Speed is the part of the job you enjoy, not the part you survive

• You'd rather ship something imperfect to ten enterprise customers this month than a perfect thing next quarter

• You don't believe in six-month roadmaps either. A plan written in January is fiction by March, and you'd rather re-plan every few weeks and be right

• You love data. Not dashboards about data — the real thing. You'd open the raw utilization numbers yourself before asking anyone for a summary, and you've been known to find the bug in them

• Hard scale problems are the fun part. Billions of rows, queries that have to come back before the coffee's poured, and a correctness bar that doesn't move

• You want to sit across from the sharpest platform and Kubernetes people on earth. They're opinionated, they'll argue with you, they'll be right about half the time — and you'd rather have that conversation than a polite one

• When something brand new and undefined turns up — a provider nobody has integrated, a gateway nobody has modelled, a cost structure nobody has seen — you want it on your desk, not someone else's

• You already use AI to do real work, not to look busy. You've built with it, and you can tell output from value

• You're comfortable being the most junior person in one room and the most senior in the next, on the same day

And probably not, if:

• You need a stable roadmap, a quiet quarter, and a clear lane

• Your favorite part of product is the process

• You want to be handed requirements, or hand them off

• You're looking for a "strategic role" — meaning a seat further from the building

• Your AI experience is mostly having opinions about AI

• "It's not defined yet" reads to you as a problem rather than an opening

What you bring

• 3+ years in product roles is a must. Devops/SRE/strong data background is a major plus.

• Technically hungry. You go deeper than the job strictly requires, and when you hit something you don't know — how a scheduler places pods, how a new provider bills, how a metrics pipeline drops data — your instinct is to go learn it this week, not to find someone who already knows

• You can take a messy data question and turn it into a detection a customer trusts enough to act on

• Independent by default. You don't wait to be unblocked, prioritized for, or told what happens next — you find out and go

You don't need to have done this exact job before. Nobody has. We care more that you can learn a system faster than anyone expects you to.

How to apply

Skip the cover letter. Tell us about something you shipped that forced you to understand a system nobody else on the team wanted to touch.

Our Values

We're a hybrid company with a big vision and a startup soul. If you’re excited to help shape the future of cloud infrastructure and join a team that cares deeply about what (and how) we build—we’d love to meet you.