Backend Engineer, Issuing

Stripe
London
9 months ago
Create job alert

Who we are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

About the team

Stripe builds economic infrastructure for the internet. We’re moving beyond payments and “up the stack” to help our users run their businesses more effectively. Issuing is developing APIs and full stack offering to empower businesses to launch physical and virtual card programs. We aim to help businesses deploy novel use cases that rely on fully programmable funds management instruments.

You can read more here:

Product pages: &
Recent News:

What you’ll do

As a backend engineer, you will design and build platforms, tooling, and system solutions that are configurable and scalable around the globe. You will partner with many functions at Stripe, with the opportunity to both work on infrastructure/platform systems, as well as produce direct user-facing business impact.

Issuing is one of Stripe’s biggest bets. The right engineer will allow us to scale faster and more reliably as our business accelerates. If that sounds exciting, we’d love to speak with you.

Responsibilities

Design, build, and maintain large-scale production services, data pipelines, and streaming systems
Work on systems critical to Stripe’s current and future operation, with responsibility for billions of dollars of issuing volume
Debug production issues across services and multiple levels of the stack
Collaborate with stakeholders across the company including engineering, product, operations, finance, data science, accounting, sales, and operations.
Improve engineering standards, tooling, and processes

Who you are

We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Minimum requirements

4+ years of experience working as a software engineer 
Have delivered, extended, and maintained large scale distributed systems.
Love to design systems that are elegant abstractions over complex patterns/practices
Experience mentoring and growing junior engineers

Preferred qualifications

You think of yourself as entrepreneurial and enjoy moving quickly on new, green-field products
You hold yourself and others to a high bar when working with production systems
You enjoy working with a diverse group of people with different expertise. Almost every role at Stripe collaborates with some engineers, from Sales and Support in sharing feedback from our customers, to Legal and Accounting in supporting our systems for tracking money movement and reporting around the world

Hybrid work at Stripe

Office-assigned Stripes spend at least 50% of the time in a given month in their local office or with users. This hits a balance between bringing people together for in-person collaboration and learning from each other, while supporting flexibility about how to do this in a way that makes sense for individuals and their teams.

Pay and benefits

The annual salary range for this role in the primary location is £72,000 - £108,000. This range may change if you are hired in another location. For sales roles, the range provided is the role’s On Target Earnings (“OTE”) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. This salary range may be inclusive of several career levels at Stripe and will be narrowed during the interview process based on a number of factors, including the candidate’s experience, qualifications, and specific location. Applicants interested in this role and who are not located in the primary location may request the annual salary range for their location during the interview process.

Specific benefits and details about what compensation is included in the salary range listed above will vary depending on the applicant’s location and can be discussed in more detail during the interview process. Benefits/additional compensation for this role may include: equity, company bonus or sales commissions/bonuses; retirement plans; health benefits; and wellness stipends.

Related Jobs

View all jobs

Senior Backend Engineer - Data Engineer

Mid/Senior Backend Engineer (Node.js & TS)

Senior Backend Engineer

Senior Backend Engineer

Senior Golang Engineer – $50 million Series B

Applied AI Engineer

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine‑Learning Jobs for Non‑Technical Professionals: Where Do You Fit In?

The Model Needs More Than Math When ChatGPT went viral and London start‑ups raised seed rounds around “foundation models,” many professionals asked, “Do I need to learn PyTorch to work in machine learning?” The answer is no. According to the Turing Institute’s UK ML Industry Survey 2024, 39 % of advertised ML roles focus on strategy, compliance, product or operations rather than writing code. As models move from proof‑of‑concept to production, demand surges for specialists who translate algorithms into business value, manage risk and drive adoption. This guide reveals the fastest‑growing non‑coding ML roles, the transferable skills you may already have, real transition stories and a 90‑day action plan—no gradient descent necessary.

Quantexa Machine‑Learning Jobs in 2025: Your Complete UK Guide to Joining the Decision‑Intelligence Revolution

Money‑laundering rings, sanctioned entities, synthetic identities—complex risks hide in plain sight inside data. Quantexa, a London‑born scale‑up now valued at US $2.2 bn (Series F, August 2024), solves that problem with contextual decision‑intelligence (DI): graph analytics, entity resolution and machine learning stitched into a single platform. Banks, insurers, telecoms and governments from HSBC to HMRC use Quantexa to spot fraud, combat financial crime and optimise customer engagement. With the launch of Quantexa AI Studio in February 2025—bringing generative AI co‑pilots and large‑scale Graph Neural Networks (GNNs) to the platform—the company is hiring at record pace. The Quantexa careers portal lists 450+ open roles worldwide, over 220 in the UK across data science, software engineering, ML Ops and client delivery. Whether you are a graduate data scientist fluent in Python, a Scala veteran who loves Spark or a solutions architect who can turn messy data into knowledge graphs, this guide explains how to land a Quantexa machine‑learning job in 2025.

Machine Learning vs. Deep Learning vs. MLOps Jobs: Which Path Should You Choose?

Machine Learning (ML) continues to transform how businesses operate, from personalised product recommendations to automated fraud detection. As ML adoption accelerates in nearly every industry—finance, healthcare, retail, automotive, and beyond—the demand for professionals with specialised ML skills is surging. Yet as you browse Machine Learning jobs on www.machinelearningjobs.co.uk, you may encounter multiple sub-disciplines, such as Deep Learning and MLOps. Each of these fields offers unique challenges, requires a distinct skill set, and can lead to a rewarding career path. So how do Machine Learning, Deep Learning, and MLOps differ? And which area best aligns with your talents and aspirations? This comprehensive guide will define each field, highlight overlaps and differences, discuss salary ranges and typical responsibilities, and explore real-world examples. By the end, you’ll have a clearer vision of which career track suits you—whether you prefer building foundational ML models, pushing the boundaries of neural network performance, or orchestrating robust ML pipelines at scale.