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Nous

Operations & Automation Associate

Nous HQ Posted today

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Sponsorship verdict

No sponsorship evidence yet

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  • No government sponsor record hereThis employer posted directly and does not match a government sponsor register.
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  • Confirmed live todayWhen a source last listed this job as open.
Details and sources
No salary bar for this route
H-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.

US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).

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Before you apply

Sponsorship unknown

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About the role

About Nous Nous is a household AI agent. It takes over the bills and admin people don't have time for and acts for them: switching energy, buying insurance, cancelling forgotten subscriptions, managing the family calendar. Nearly 250,000 UK households have signed up, and another joins every minute. We think the real value of AI isn't writing amazing cover letters, it's doing useful things in the real world for real people. That's the hard part, and a brilliant product is only half of it. An energy switch takes weeks, so our agents run on long-running workflows, not a single chat turn. Cancelling a subscription means a browser agent handling the code Apple sends to your phone. Buying insurance meant getting our regulatory principal comfortable with an agent doing it end to end (a UK first), which comes down to knowing exactly when the LLM should hand over to rules because a mistake would cost someone real money. We're about 30 people running one agent across energy, broadband, mobile, insurance, mortgages and subscriptions (with more being added), each with its own suppliers, rules and ways of going wrong. With far more problems than people, a person or small team owns each one end to end, from spotting it in the data or a customer conversation to shipping the fix. And because the agent acts, you find out quickly whether the fix worked. Everyone builds here: a product engineer launched our subscription cancellation product within a couple of months of joining, and a PM rebuilt our WhatsApp agent. And it's working. We're growing more than 20x year on year, with a market-leading NPS above +80, and we manage well over half a billion pounds of household spending. We've raised more than $30M from Heartcore, Mosaic and a who's who of entrepreneurs who founded Monzo, GoCardless, lastminute.com and Onfido. But the real draw is the team - ask anyone here why they joined and you'll get some version of "to do great work with really smart people". The goal is household life that's simpler, cheaper and fairer, and most of that is still to build. The role What Nous does for members can feel simple: helping them understand their bills, save money, and avoid admin. Delivering that reliably at scale is much harder. You’ll join the Operations team at the heart of the business, working on the difficult real-world problems that sit between our members, our product, external suppliers and the systems that make Nous work. This is not a traditional customer operations role. You’ll spend time close to our members, understanding their questions, investigating things that have gone wrong and learning in detail how our service works. We think that matters: the best systems are usually built by people who have seen the underlying problems first-hand. But we don’t want you simply to get faster at doing manual work. We want you to spot the pattern, understand why the problem exists, and help build a better way of doing it. That might mean redesigning a process, building an automation, using AI to remove repetitive work, querying data to understand what is happening, improving an internal tool, or working with Product and Engineering on a more fundamental fix. You don't need to arrive knowing all of the tools we use. We care much more about how quickly you learn them. We’re looking for someone early in their career who is unusually curious, quick to learn and good at figuring things out. You might already have built automations or small products yourself. You might instead have worked in operations, consulting, product, analytics or another demanding environment and found yourself constantly looking for better ways to do things. Either way, we want someone who gets excited by a problem they don't immediately know how to solve. You’ll have a lot of support from

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