Careers
We work where the usual answers stop working.
Terabytes and up. Billions of rows. Telemetry with cardinality nobody planned for. At that scale the interesting question is never which framework — it is what the machine actually has to read, and how you make it read less. If that is the part of the job you like rather than tolerate, we should talk.
Open roles
Two, and both are foundational.
Solution Architect
You own the shape of an engagement end to end. Profile the workload, design the target architecture, work out how it integrates with everything the client already runs, and defend all of it in front of their technical leadership.
Read the posting →Senior Data Engineer
Engine selection, sort key and partition design, materialized views, ingestion and integration paths, and the compression work that produces the cost result. On data large enough that the decisions actually matter.
Read the posting →About Granule Labs
Granule Labs is a startup AI-native data engineering firm. We build the data layer that agents can actually use. That is the opportunity, and it is a large one. An organization's data is not ready for AI until it can be queried at agent speed, at agent volume, with meaning attached, in real time, at a cost that does not scale with usage. Five conditions. Most organizations meet none of them.
We do two things. We enable the data layer for AI — terabytes of real-time data that answer in milliseconds, and observability priced so you keep every log instead of aggregating away the one that would explain the outage. And we build the team around it — a small senior team embedded beside them, a development platform your client runs, and advisory that aids in ranking client decisions by their value.
We are standardizing on ClickHouse. Not out of loyalty — because of how it reads, how it stores, and how it performs. We are equally willing to say it is the wrong tool.
We are backed by Pure Play Partners and Gryphon Investors and led by people who have built and exited services companies. This is the beginning of something amazing, and the people we hire now will define who we become.
How we work
Small senior teams, with AI in the loop, pointed at workloads where the value is large.
It is the most fun version of this job we know how to build: four people, twelve weeks, and a result the clients can measure.
Everyone here works on the same clients. We sell together, build together, and stay until it is running. Nobody sells a client an impossible promise and then disappears, because the person who scoped it is on the engagement. There is no matrixed reporting, no account layer, no enterprise reporting apparatus consuming a day a week, and no overhead you would be paying for. We do a small number of things and we point all of us at them.
Every engagement starts from an offering, not from a blank page: reference architectures, workload profiling, and a cost model ready to run on day one. Clients pay for the answer, not for inventing the method.
What we look for in everyone
You have worked on genuinely large systems.
Not a large company — large data. You have felt a query plan go wrong at scale, watched a migration run for days, and had to answer for the bill afterward.
You have made data fast for a real reason.
Diagnosed a workload, changed how the data was laid out, and watched the number move. Benchmarking someone else's dataset does not count.
You are genuinely interested in what AI changes here.
Agent-driven query load is a different engineering problem, and the tooling is moving fast enough that curiosity is a job requirement rather than a personality trait.
You treat this as a craft.
You keep pulling the thread after the ticket is closed, because you want to know what the answer actually was.
You can say "this is the wrong tool" to a paying client.
Our differentiator is that we say it. That only works if the people doing the work will.
You do not have a lane.
This is an early company. Some weeks you are writing the reference architecture; some weeks you are fixing the demo environment at 11pm because the client call is at 8am.
We were raised in the Midwest and are looking for people who out-work the problem rather than out-talk it. This should describe how you show up, not how many hours you put in. The work is hard enough without pretending exhaustion is a credential.
Where we are probably wrong for you: if you want a defined lane, a long predictable runway, or a large team around you, this will be uncomfortable. That is not a judgment — it is a real difference, and it is cheaper to find out now.
Location, travel and benefits
Based in Minneapolis, MN or Seattle, WA. Some client travel should be expected; it varies by engagement and is not the majority of the job.
Base salary ranges are stated on each posting. Eligible for an annual performance bonus and other perks.
Actual compensation depends on job-related skills, depth of experience, certifications, and location.
Benefits include medical, dental, and vision coverage; a 401(k) with company contribution; 4 weeks paid vacation + holidays; tools needed to accomplish your job (including Claude / OpenAI subscriptions); and an annual budget for conferences, certifications, and hardware.
How we hire
Hiring is the most important thing that we do; our product is our people, who develop our offerings and deliver for our clients.
If your resume shows you meet most of what a posting asks for, you tell us plainly why you do not meet the rest, and you can describe a system you built that resembles what we build — you will communicate immediately with a human.
Four steps after that:
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Human phone screen
A real conversation about your work, not a checklist read back to you.
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Technical screen
A short assignment, a live technical screen, or an online coding exercise, depending on the role.
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Deep dive with the CTO
Architecture, tradeoffs, and the decisions you have had to defend.
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Final conversation with the CEO
How you work with clients, and whether this is the right place for you.
Then an offer. We aim to be done quickly and to tell you where you stand at every step.
If your experience does not line up exactly with a posting, apply anyway. We would rather read your note than have you screen yourself out.
Visa sponsorship
We are not able to sponsor employment visas at this time. Applicants must be authorized to work in the United States without current or future sponsorship.
Equal employment opportunity
Granule Labs is an equal opportunity employer. We do not discriminate on the basis of age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical or mental ability, political affiliation, race, religion, sexual orientation, socioeconomic status, veteran status, or any other characteristic protected by applicable law. We are committed to providing reasonable accommodation to applicants with disabilities — email careers@granulelabs.ai and we will work it out.
A note on recruiting fraud
All Granule Labs recruiting correspondence comes from an @granulelabs.ai address. We will never ask a candidate for payment, banking details, or government identification during the interview process.