Senior · Удалённо · Великобритания · Английский B2
Навыки
Лидерство
Machine Learning
REST API
Transformers / HuggingFace
О компании и продукте
Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. Join Affirm as a Machine Learning Engineering Manager and become a pivotal part of our Repayment & Recovery machine learning group
Задачи
Set the technical strategy for your team, and help your engineers tie it together with critical, business-impacting projects across repayment, collections, recovery, and loss mitigation
Act as a force-multiplier through your definition and advocacy of technical solutions and operational processes
Collaborate across teams in the product development lifecycle by partnering with product management, design, analytics, and risk to ensure technical sustainability, risks and trade-offs are well understood and managed
Develop talent by providing feedback and guidance, and leading by example
Specifically, you will manage a team of ML engineers that builds the models powering the post-origination side of the credit lifecycle using novel ML techniques and rich representations of data to predict repayment behaviour, prioritise and personalise collections, and minimise loss after a loan is originated
In this role, you will help shape the future of machine learning at Affirm
You'll partner with engineering, product, and risk leaders to design, implement, and scale advanced ML solutions that drive critical capabilities across the company
You will mentor engineers, bring clarity to complex, ambiguous problems, and contribute to a cohesive long-term ML strategy
This role is based in Europe, with openness to candidates in the UK, Poland, or Spain
Требования
Bachelor's in a technical field with 8+ years of industry experience, including 3+ years managing engineers
A background in financial services, specifically in credit / lending, with hands-on experience in post-origination modeling — collections, repayment, recovery, loss mitigation, early-warning or behavioural risk, or similar
Proficiency in machine learning with experience in areas including tree-based models, transformers, deep learning, and agentic ML
Strong engineering skills and the ability to provide hands-on technical leadership while working with our code and architecture
You thrive in ambiguity, and are comfortable moving from low level language idioms all the way to the architecture of large systems to understand how they work
This position requires either equivalent practical experience or a Bachelor's degree in a related field
Условия
Base Pay Grade - P
Equity Grade - 7
Employees new to Affirm typically come in at the start of the pay range
Affirm focuses on providing a simple and transparent pay structure which is based on a variety of factors, including location, experience and job-related skills
Base pay is part of a total compensation package that may include equity rewards, monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidised medical coverage, dental and vision for you and your dependents.)
Base pay range per year: £142,000 - £190,000
Location: UK Remote
Affirm is proud to be a remote-first company!
The majority of our roles are remote and you can work almost anywhere within the country of employment
Affirmers in proximal roles have the flexibility to work remotely, but will occasionally be required to work out of their assigned Affirm office
A limited number of roles remain office-based due to the nature of their job responsibilities
We’re extremely proud to offer competitive benefits that are anchored to our core value of people come first
Some key highlights of our benefits package include
Health care coverage - Affirm covers all premiums for all levels of coverage for you and your dependents
Flexible Spending Wallets - generous stipends for spending on Technology, Food, various Lifestyle needs, and family forming expenses
Time off - competitive vacation and holiday schedules allowing you to take time off to rest and recharge
ESPP - An employee stock purchase plan enabling you to buy shares of Affirm at a discount
We believe It’s On Us to provide an inclusive interview experience for all, including people with disabilities
We are happy to provide reasonable accommodations to candidates in need of individualized support during the hiring process
For U.S. positions that could be performed in Los Angeles or San Francisco] Pursuant to the San Francisco Fair Chance Ordinance and Los Angeles Fair Chance Initiative for Hiring Ordinance, Affirm will consider for employment qualified applicants with arrest and conviction records
By clicking "Submit Application," you acknowledge that you have read Affirm's Global Candidate Privacy Notice and hereby freely and unambiguously give informed consent to the collection, processing, use, and storage of your personal information as described therein
Паспорт вакансии
История публикации
Появилась в Вакандии30 дней
Перепубликациинетпубликовалась один раз
Проверяли на источникеВидели 30 дней назад
Среди похожихНет данных172 из 30 · у похожих вакансий почти одинаковый возраст — сравнивать нечего
Откуда что взялось
Отмечено то, что вывели мы. Без пометки — значение назвал работодатель.
ГрейдSeniorвыведено из другого признака
Формат работыУдалённо
ГеографияВеликобританиявычитано из текста вакансии
Зарплата≈ 19 917 USD в месяцнаша оценка, в вакансии не названа
Почему на этом месте в выдаче
Порядок выдачи объявлен контрактом: свежесть решает между днями, полнота и зарплата — внутри дня.
Полнота карточки1004 из 4 полей: грейд, формат, география, зарплата
Зарплата названа0вилки работодателя нет, показана наша оценка
Проверка Вакандии
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Карьерный сайт работодателя
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greenhouseОсновная публикация · 2026-06-23
A
Работодатель
Affirm
50 активных вакансий · вилка работодателя указана в 54%
Вакандия показывает вакансию, но не отправляет отклик и не проверяет работодателя. Сам отклик вы оставляете на внешнем сайте — job-boards.greenhouse.io.
Признаки мошенничества
Просят предоплату, «залог» или деньги за обучение и оборудование.
Требуют код из SMS, данные банковской карты или доступ к «Госуслугам».
Быстро уводят в мессенджер и торопят с решением.
Обещают большой доход без опыта и без деталей задач.
Настоящий работодатель не просит денег и платёжных данных до трудоустройства.
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