Help build the Brain behind better organizations
The work sits at the intersection of knowledge, human expertise, AI and the daily operations of large organizations. It is unusually concrete for a category this new.
Trusted by knowledge-intensive organizations
Automotive
Hospitality
Financial Services
Telecom
Automotive
Education
Software
Financial Services
Financial Services
Retail
Automotive
Education
Insurance
Why the problem is worth your time
Large organizations have plenty of information. Understanding is the harder thing to hold: what applies, to whom, when and why, in a form somebody can act on in the moment. That gap shows up as handling time, escalations, decisions that vary by desk and AI programs that stall after the pilot. It is a large problem, and solving it changes how an organization works every day.
Here the work is visible: what we build is used by contact center agents, branch staff, field crews, students, caseworkers and AI agents, in operations where a better answer changes someone’s afternoon.
ClearMash is a game changer.
How we work
Curiosity
Understanding how a customer’s operation actually runs is the job itself.
Continuous improvement
We would rather correct something small every week than plan a large correction for next year.
Practical innovation
Ideas are judged by whether they survive a real operation.
Human-centric AI
Our AI strengthens people’s expertise.
Customer partnership
Long relationships with organizations who tell us plainly what to fix next.
Ownership
Real accountability, and a short distance between a decision and its consequences.
Who tends to do well here
People who are interested in how organizations actually work: why departments answer the same question differently, why a procedure gets skipped, why an AI agent succeeds in one place and struggles in another.
The work rewards clear thinking and clear writing. A significant part of building a Brain is deciding what something means and expressing it so that employees, customers and AI agents can all act on it.
What you would be working on
Open roles
Applying is one email: use the link on the role, or write to jobs@
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Data Scientist
Measure what a Brain is actually doing to an operation, and say so in a way the people who own the knowledge can act on.
What you would do
- Work out which questions never find an answer, where a wrong one came from, and what moved in a business area after it came onto the Brain.
- Build the measures a knowledge owner can act on, and check that they still mean this quarter what they meant last quarter.
- Sit with the operation you are measuring, because the number that matters is rarely the one that is easy to collect.
What you would bring
- Applied statistics, and the judgment to tell a real difference from a coincidence.
- SQL and Python, and the patience for data that was never collected with your question in mind.
- Writing a head of service reads once and acts on.
Why here. You would be putting numbers on one of the most valuable questions in enterprise AI: what better knowledge is worth to an operation, and how an AI agent performs when its knowledge improves. Your findings go to the people who own that knowledge, and the change they make is the next thing you measure.
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AI Engineer
Build how AI agents use the Brain, and prove what they do with it before a customer’s case depends on it.
What you would do
- Define what an AI agent may reach, where it has to stop, and the point where a service agent takes over.
- Build the evaluation that catches a bad answer before anyone acts on it, and keep it running release after release.
- Work with knowledge engineers so an AI agent follows the same method as the people beside it.
What you would bring
- Production experience with language models, and the scars from evaluating them.
- Python, and a working grasp of the systems around the model rather than the model alone.
- The instinct to distrust a demo that worked once.
Why here. You would be working on cutting-edge AI where it meets real organizational knowledge: deciding what an AI agent may act on, where it hands a case to a service agent, and how it shows its answer is right. The cases it answers belong to banks, hospitals and telecoms.
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Backend Engineer
The platform under all of it: the model, the APIs, the events and synchronization, and the environments customers run in.
What you would do
- Build and keep the services the Brain runs on, from the knowledge model to the integration surface other teams build against.
- Design for the failure case first: what happens when a source system is slow, a sync is late or a release has to go back.
- Carry the performance an enterprise never mentions until the day it goes missing.
What you would bring
- Several years of server-side engineering, and opinions about data modeling you can defend.
- APIs, events and synchronization for other teams to build on.
- Care about the boundary: what belongs in the Brain and what belongs in a system of record.
Why here. You would be building a new kind of enterprise system: one understanding of an organization, delivered to every channel, every employee and every AI agent, with a dated record of what it said. The architecture you choose here helps define what enterprise knowledge orchestration becomes.
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Front-end Engineer
The interfaces people actually work in, on a bad day, at speed, with a customer waiting.
What you would do
- Build the answer read mid-call, the editor a knowledge owner corrects a procedure in, and the self-service page a customer lands on at eleven at night.
- Hold accessibility and performance as requirements of the work from its first line.
- Make one organizational meaning feel right in each of them.
What you would bring
- Strong JavaScript, and views about HTML and CSS.
- Experience of an application people use all day rather than a page they visit once.
- Accessibility you have actually shipped, keyboard and screen reader included.
Why here. You would be designing the moment people meet an organization’s knowledge when it matters most: mid-call with a customer waiting, on a phone in the field, in a help center at midnight. A good screen here changes how fast and how confidently whole teams do their work.
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QA Engineer
Decide what working means for a system that answers questions, then hold it there.
What you would do
- Define the behavior a release has to keep across channels, languages and audiences, and build the suite that proves it.
- Test the knowledge path as well as the code path: a correct system serving an out-of-date answer is still a defect.
- Work inside the certified quality process the platform is audited against.
What you would bring
- Test design that starts from risk rather than from a feature list.
- Automation, and the judgment to know what should stay in a tester’s hands.
- Written English precise enough that anyone can act on your defect report.
Why here. You would be deciding what correct means for a system that answers employees, customers and AI agents, where one answer has to hold in every channel and every language. Quality here covers the knowledge as well as the code, so your suite guards what an organization says as closely as what the software does.
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Product Manager
Own a part of the Brain end to end: what it is for, where its edges are, and what happens next.
What you would do
- Turn what an operation actually does into something the product can hold, and keep the two honest with each other.
- Choose which of the reasonable customer requests is the one that matters, and write down why the rest are waiting.
- Keep engineering, delivery and sales arguing about the same thing.
What you would bring
- Enterprise software shipped to people who use it as their job.
- The discipline to say no clearly, in the room, with a reason.
- Enough technical depth to be useful in a design discussion.
Why here. You would be shaping a product in a category that is still being written: enterprise knowledge orchestration. What you decide the Brain should do, and how it describes itself, sets the direction for how large organizations put their knowledge to work, for their people and their AI agents alike.
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Knowledge Engineer
Turn an organization’s material into knowledge that can be acted on: what applies, to whom, when and why.
What you would do
- Assess what exists: what is current, what conflicts, what is missing, and what cannot be applied as written.
- Model a subject so that one meaning serves an employee, a customer and an AI agent, written once.
- Argue with a subject expert until a rule is stated so that an employee and an AI agent can both follow it.
What you would bring
- Unusually clear writing, and the patient analysis that has to happen before it.
- Comfort interviewing experts who are busy and certain.
- Structure: the instinct to model a subject rather than to file a document.
Why here. You would be turning an organization’s expertise into knowledge that people and AI agents can both use, which is what enterprises need from AI now. You work with the experts of banks, hospitals and telecoms, and what you write is what their front line, their customers and their AI agents act on.
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Solutions Architect
Sit inside a prospect’s operation and work out what a Brain would change in it, and where.
What you would do
- Design the first business area, the integrations it needs and the governance it has to satisfy.
- Answer the security and architecture review with the platform’s real properties, including the parts that are limits.
- Say when the answer is no, and why, before anyone signs anything.
What you would bring
- Enterprise integration experience: identity, APIs, events and systems of record.
- Presence with both a head of service and a security architect, in the same hour.
- The confidence to disagree with your own salesperson, with the customer in the room.
Why here. You would be designing how a Brain fits into complex enterprises: their systems, their security reviews, their regulators. Each design is a new answer to how an organization gets the most out of its knowledge, and you see it from the first workshop to the day it runs.
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Onboarding Consultant
Bring a business area onto the Brain and stay until it is genuinely being used rather than merely delivered.
What you would do
- Assess the material that exists, improve it on the way in, and set up who owns what afterwards.
- Train the people who will keep the knowledge current, in their own vocabulary.
- Send back what the operation taught you, so the product can learn from it.
What you would bring
- Delivery or consulting experience inside a large organization, and the patience it teaches.
- Enough product depth to configure something rather than to request it.
- Comfort standing in a contact center, a branch or a depot and asking why.
Why here. You would be turning a knowledge program into daily practice, alongside the people who do the work: in a contact center, a branch or a depot. It is where an enterprise first sees what its own knowledge can do, and what you learn there shapes the product.
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Customer Success Manager
Long relationships with large organizations, built on hearing what to improve and improving it.
What you would do
- Watch adoption where it is real: which teams use the Brain in the work, and which still prefer to ask the colleague at the next desk.
- Open the hard conversation early, while a knowledge program can still change course.
- Find the next business area, and make the case for it out of what the first one changed.
What you would bring
- Ownership of enterprise accounts well after the sale.
- The ability to read an operation’s numbers and ask a better question about them.
- Steadiness: the good version of this job is years long.
Why here. You would be helping enterprises get the most out of their knowledge, year after year: the next business area, the next audience, the next AI agent to work from the Brain. The organizations are large, the results show in their operations, and the relationship grows with every area you add.
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Enterprise Account Executive
Long, technical, multi-stakeholder sales into banks, hospitals, public bodies, universities and technology companies.
What you would do
- Find the operation where unclear knowledge is already costing something, and build the case around that cost.
- Bring security review, procurement and a proof of value into the plan from the start.
- Qualify hard, and put your time where a Brain changes the most.
What you would bring
- A record of closing complex enterprise software, with the security and procurement track to show for it.
- Willingness to learn how a customer’s operation runs, well past its org chart.
- Straight talk, and the patience to sell a product on its detail.
Why here. You would be selling a new category to banks, hospitals, public bodies and technology companies as they decide how AI will use their knowledge. The conversations are strategic, the stakes are the whole operation, and what you sell changes how an organization works.
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Technical Support Engineer
Answer the customer’s own engineers and administrators: diagnose, reproduce, explain, and get the fix into a release.
What you would do
- Take an unclear report and turn it into a reproduction an engineer can act on.
- Give the customer the workaround today and the fix its place in a release.
- Write the answer where the next colleague or customer will find it.
What you would bring
- Debugging across an application, its integrations and the systems around it.
- Written English that calms an engineer on a bad day.
- The habit of turning one answer into documentation rather than into a second ticket.
Why here. You would be the expert behind the experts: the engineers and administrators who run a Brain for their organization. Every case you solve becomes knowledge their teams and AI agents use, so an answer from you travels far beyond the ticket, and into the next release.
Send us your CV
Write to jobs@
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ClearMash is an equal opportunity employer and embraces and encourages our differences in age, race, ability, ethnicity, family or marital status, gender identity or expression, language, national origin, political affiliation, religion, sexual orientation, socio-economic status, veteran status, and other characteristics and life experiences that make each of us unique. If you need reasonable accommodation at any point in the application or interview process, please let us know.
Different roles. One unusually concrete problem.
Pick the one that is recognizably you and write to us, or tell us which part of the problem you would want to work on.