---
title: 'Senior AI Engineer at Gradient Labs'
canonical: 'https://feeny.ai/job/senior-ai-engineer-gradient-labs-london-dpeyvbcrs0b0'
type: 'job'
last_seen: '2026-09-07'
---

# Senior AI Engineer at Gradient Labs

- **Company:** Gradient Labs
- **Location:** London, United Kingdom
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-09-01
- **Last confirmed live:** 2026-09-07
- **Apply:** https://jobs.ashbyhq.com/gradient-labs/b2f42c6a-006a-4328-a205-6d0da36298ea

## Job description

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

## About Gradient Labs

## Company Overview
- **One-liner**: Gradient Labs builds AI-native customer operations agents that automate complex, regulated workflows for financial services.
- **Entity Type**: Private (Series A, $13 M led by Redpoint in 2025; total funding $26 M per website, $16.6 M per LinkedIn)
- **Headquarters**: London, United Kingdom
- **Founded**: 2023
- **Founders**: Dimitri Masin (CEO), Neal Lathia (CTO), Danai Antoniou (Chief Scientist) – all former leaders at Monzo

## Core Business
- **Primary industry**: AI‑powered customer operations for financial services (fintech, banking, lending, insurance)
- **Target customers**: B2B / Enterprise – financial institutions, neobanks, fintechs (e.g., Yonder, Plum, Sling Money, Nala, Penfold, Zego)
- **Mission**: Deliver exceptional, compliant customer service at scale by automating the most complex operational workflows in regulated industries.

## Products & Services
- **AI Agent Suite**: A set of specialist agents for **Collections, Disputes, KYC/KYB, Onboarding, Customer Service, and Insurance Claims**. Each agent is purpose‑built for long‑running financial processes, runs 20+ guardrails per turn, and is pre‑configured with global regulatory compliance (FCA, CONC, Reg E/Z, PSD2, GDPR, EU AI Act).
- **Type**: SaaS / API‑first – integrates via API, CSV, or existing tools (e.g., CRM, core banking). Supports email, text, and voice channels. Multi‑model AI (OpenAI, Anthropic, Google) with enterprise‑grade security (SOC 2 Type 2, SSO, audit logs).

## Market Standing
- **Valuation**: Not publicly disclosed
- **Key Metric**: Total funding of **$26 M** (website) / **$16.6 M** (LinkedIn); annual revenue not public
- **Notable Investors/Partners**: Redpoint (Series A lead), LocalGlobe (Seed lead), plus 12+ additional investors
- **Growth Signals**: 
  - 40+ employees with 150 % YoY headcount growth (LinkedIn: 39 employees)
  - Reported 98 % CSAT, 32 M customers served, 80‑90 % automation rate in collections
  - Clients include notable UK fintechs; expanding into US market (open roles for US-based enterprise account executives)

## Competitive Advantages
- **Regulatory moat**: Provably compliant by design – 20+ guardrails on every interaction, pre‑configured for US, UK, and EU regulations (FCA, PSD2, GDPR, EU AI Act, Reg E/Z)
- **Enterprise readiness**: SOC 2 Type 2 certified, SSO, comprehensive audit logs, role‑based permissions
- **Multi‑model AI**: Pipeline uses latest from OpenAI, Anthropic, and Google – rapid model switching without service disruption
- **Learns from real operations**: Trained on SOPs and best human agents, not just help‑centre content – leads to human‑like conversations and 98 % CSAT

## Strategic Focus
- **Product expansion**: Deepening automation across the full customer operations lifecycle (collections, disputes, KYC, onboarding) and adding new use cases (insurance, KYB)
- **Geographic growth**: Targeting US financial institutions (active hiring for US enterprise sales) while maintaining strong UK/Europe roots
- **Platform vision**: Becoming the “operating system for customer operations” by connecting AI agents to core financial systems, not just help desks

## Why Work Here
- **Culture**: Remote‑first with weekly in‑person meetups in central London; founded by ex‑Monzo leaders who built world‑class ML and data teams
- **Work model**: Hybrid / remote across the UK; open to hiring across the UK and US (some roles fully remote)
- **Perks**: Competitive salary, significant equity, private medical insurance, 25 paid days off + 20 optional unpaid days, regular team socials
- **Engineering**: Strong ML‑first culture – technical team includes former Monzo data scientists; interview process includes a practical task and conversations with stakeholders (typically 4 stages)

## Sources
1. [gradient-labs.ai](https://gradient-labs.ai/)
2. [Gradient Labs Careers](https://jobs.ashbyhq.com/gradient-labs)
3. [Gradient Labs About](https://gradient-labs.ai/about)
4. [LinkedIn Company Page](https://www.linkedin.com/company/gradientlabs)

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