Asks for

The ad says“Professional software engineering experience, with a meaningful focus on Machine Learning, NLP, or applied AI. At this time, we need the ML/applied AI experience as a non-negotiable requirement for this role.”
The ad says“Experiment and prototype: Keep up with the latest in NLP, agentic systems, and generative AI. Prototype against our hardest problems with a bias toward shipping experiments quickly rather than long research cycles. Our agents already handle tens of thousands of real customer conversations every hour, so your work has immediate, visible impact.”
The ad says“Hands-on experience building with LLMs, whether in a previous role, at a startup (even one that didn't work out), or on a small, scrappy team.”
The ad says“Professional software engineering experience, with a meaningful focus on Machine Learning, NLP, or applied AI. At this time, we need the ML/applied AI experience as a non-negotiable requirement for this role.”
The ad says“Shape our internal AI platform: Contribute to shared libraries, patterns, and standards for how we build, evaluate, and deploy agents across customers. Help define how we approach prompting, tool orchestration, retrieval, and monitoring.”
The ad says“Professional software engineering experience, with a meaningful focus on Machine Learning, NLP, or applied AI. At this time, we need the ML/applied AI experience as a non-negotiable requirement for this role.”
The ad says“This is a build-and-ship role. You'll turn ambiguous customer support problems into reliable, observable AI agents that handle live conversations for real users. You'll work close to production - designing prompts and tool flows, building eval suites, shipping changes, watching what breaks, and iterating fast.”
The ad says“Shape our internal AI platform: Contribute to shared libraries, patterns, and standards for how we build, evaluate, and deploy agents across customers. Help define how we approach prompting, tool orchestration, retrieval, and monitoring.”
The ad says“Shape our internal AI platform: Contribute to shared libraries, patterns, and standards for how we build, evaluate, and deploy agents across customers. Help define how we approach prompting, tool orchestration, retrieval, and monitoring.”

At Gradient Labs, we're building the AI customer operations platform for financial services.

Founded in 2023, we now work with some of the biggest names in banking and fintech. Our platform runs specialist agents, purpose-built for financial services, to eliminate manual work across customer support and back-office operations. Together, they give product and operations teams the visibility and control to trust every outcome.

We're a team of builders from companies like Monzo, Wise, Revolut and Google. If you're excited to tackle some of the hardest problems in AI and help shape the future of customer operations, we'd love to hear from you.

🎯 How you’ll make an impact

This is a build-and-ship role. You'll turn ambiguous customer support problems into reliable, observable AI agents that handle live conversations for real users. You'll work close to production - designing prompts and tool flows, building eval suites, shipping changes, watching what breaks, and iterating fast.

Build and operate AI agents in production: Design, implement, and maintain agentic systems powered by LLMs - handling tool calling, multi-step reasoning, and integration with customer APIs and data sources. You'll own these systems end-to-end: reliable, observable, and auditable from day one.
Translate business problems into agentic workflows: Take on ambiguous, open-ended problems (like "help our agent handle conversations in multiple languages") and turn them into scoped, shippable projects.
Strong product mindset: Prioritise product thinking over pure ML technique, optimising for customer and business value rather than model performance for its own sake.
Build robust evaluation infrastructure: Create and maintain eval suites drawn from real-world scenarios and edge cases. Go beyond vibes-based testing: structured evals measuring accuracy, safety, and latency, tied to clear business outcomes, used to drive systematic improvements to prompts, tools, and behaviour.
Enhance our agent: Develop, evaluate, and optimise the skills that make up our agent. Curate datasets, iterate on improvements, test changes, and ship successful approaches into production.
Shape our internal AI platform: Contribute to shared libraries, patterns, and standards for how we build, evaluate, and deploy agents across customers. Help define how we approach prompting, tool orchestration, retrieval, and monitoring.
Experiment and prototype: Keep up with the latest in NLP, agentic systems, and generative AI. Prototype against our hardest problems with a bias toward shipping experiments quickly rather than long research cycles. Our agents already handle tens of thousands of real customer conversations every hour, so your work has immediate, visible impact.
Analyse data: Work across customer queries, support tickets, and related data to find patterns and identify what our agents could automate next.
Drive technical decisions: Scope your own work, push back when the framing is wrong, and tell us when the plan needs to change.

💡 What you’ll bring

Professional software engineering experience, with a meaningful focus on Machine Learning, NLP, or applied AI. At this time, we need the ML/applied AI experience as a non-negotiable requirement for this role.
Experience shipping products to real, live customers, not just internal tools or prototypes, ideally at meaningful scale.
A strong product mindset - you can take a vague problem, break it down from first principles, and know how to get to a valuable first version. You have a preference for fast iteration over long research cycles.
Hands-on experience building with LLMs, whether in a previous role, at a startup (even one that didn't work out), or on a small, scrappy team.
Comfort with ambiguity, and the confidence to say "I don't understand" and work through it rather than guessing. You can take open-ended problems (like "help our agent handle conversations in multiple languages") and turn them into scoped, shippable projects.
Strong communication skills - you can explain the reasoning and trade-offs behind your decisions, not just describe what you built, you communicate clearly and often, and you flag early when you’re stuck.
A pragmatic, tech-agnostic approach - no specific tech stack required, just good judgement about what's right for the problem.

Why join Gradient Labs?

This is a unique chance to be part of a team working with cutting-edge technology to reshape how businesses will operate in the future. Over the next 10 years, every company will need to embrace AI-powered operations to stay competitive, and this role puts you right in the middle of that transformation.

You’ll tackle challenging and new problems, work with some of the most exciting brands across different industries, and be surrounded by a passionate, smart team that’s driven to build something groundbreaking.

Benefits & Logistics 🧡

We’re still at early stages of building out our compensation bandings, whilst we continue to shape this, we’re open to chatting with people who have a range of salary requirements
- Our compensation package has two key components - base salary & meaningful equity
Private medical & dental insurance
25 days holiday + bank holidays (or bank holiday opt out for 32 days holiday)
Team socials and offsites
Flexible working - we have an office in central London and a hybrid working approach. This role is open to London based or remote in the UK folks - please note, we'd be looking for the person in this role to be able to come into the London office twice per week

The Interview Process 📝

30 mins Talent Screen with our Founding People & Talent
45 mins First Stage interview with one of our AI Engineers to talk through a complex AI project you've worked on
Take Home Task
1hr Mid-stage Interview to discuss the take home task you've completed with our Chief Scientist
1hr Final Interview focusing on product thinking