Senior Staff Software Engineer, Affirm
Backend, Machine Learning Platform
$232-310k
Salary range dependent on candidate location. Plus equity and ESPP
POS installment loans
Job no longer available
POS installment loans
1001+ employees
Job no longer available
$232-310k
Salary range dependent on candidate location. Plus equity and ESPP
1001+ employees
Company mission
To deliver honest and transparent financial products that improve users' lives.
Role
Who you are
- 10+ years of experience designing, developing and launching backend systems at scale using languages like Python or Kotlin
- Strong experience leading multiple engineering teams to deliver high quality software
- Track record of successfully leading engineering teams at both rapidly scaling startups and complex larger technology companies
- Expertise in synthesizing complex technical requirements, designs, trade-offs, and capabilities into clear decisions to influence ML & engineering direction
- Extensive experience developing highly available distributed systems using technologies like AWS, MySQL, Spark and Kubernetes
- Experience building and operating online, real-time ML infrastructure including a model server and a feature store
- Experience developing an offline environment for large scale data analysis and model training using technologies including Spark, Kubeflow, Ray, and Airflow
- Experience delivering major features and system components
- 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
- Your growth and impact trajectory demonstrates that you have mastered gathering and iterating on feedback from your engineering and cross-functional peers
- You have strong verbal and written communication skills that support effective collaboration with our global engineering team
- This position requires either equivalent practical experience or a Bachelor’s degree in a related field
What the job involves
- The ML Platform team is on a mission to build the core infrastructure that powers Affirm's intelligence
- Affirm uses machine learning to assess and approve each BNPL transaction
- Affirm’s ML models answer questions like: is the applicant willing and able to repay this loan?
- The role of the ML Platform team is to build the compute platform for training and serving all of Affirm's ML models and features
- Online, we operate a feature store and model server that enable real-time feature computation and model scoring
- Offline, we manage an environment for running large scale model training and data analysis
- You will set the multi-year, multi-team technical strategy for ML Platform and deliver it through direct implementation or broad technical leadership
- You will partner with technical leaders across the company to create joint roadmaps that will achieve business impacting goals through the advancement of machine learning
- You will act as a force-multiplier for your team through your definition and advocacy of technical solutions and operational processes
- You have an ownership mindset, and you will proactively champion investments in availability so that every project in your area achieves its availability targets
- You will foster a culture of quality and ownership on your team by setting code review and design standards for your team, and advocating for them beyond your team through your writing and tech talks
- You will help develop talent on your team by providing feedback and guidance, and leading by example
Our take
Affirm offers point-of-sale loans to consumers, working with merchants like Shopify, KAYAK, and Walmart. Buy now, pay later transactions have grown in popularity in recent years, and this business is tapping into the market with a totally different approach to credit - by delivering the Affirm card, the first US debit card with the option to pay-over-time.
Boasting over 21M consumers and 337,000+ merchants, the company offers flexible payback options, ranging from 3 to 36 months. The interest rates run between 0% and 30%, so, while they are not necessarily the cheapest option, Affirm prides itself on having no fees for late payments, no hidden charges, and total transparency about what people will pay before they take out a loan.
Affirm collects payments from the customers and pays the merchants directly, shouldering the risk itself. The company has boosted its market share, expanding its availability beyond the US to include most of Canada, and continues to launch new features and create new partnerships towards bettering its offering.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
-11% employee growth in 12 months
Company
Funding (last 2 of 5 rounds)
Sep 2020
$500m
SERIES G
Apr 2019
$300m
SERIES F
Total funding: $1.1bn
Company benefits
- Compensation: We have a simple, flexible, and transparent remote-first compensation structure so you can make the best decisions for yourself and your family
- Spending Wallets: Access tech, food, lifestyle, and family planning wallets for your expenses
- Supportive Communities: Get involved with our employee resource groups and community groups
- Remote-first Workforce: If your role is remote, you can set up shop anywhere in your home country
- Generous Time Off: Take the time you need when life happens
- Health Benefits: Get a plan that fits your needs
- Mental Healthcare: Take care of your mind with great mental health programs
- Parental Leave: Birth and non-birth parents get 18 weeks’ paid leave. Plus, a 4-week return-to-work transition program, at full base pay
- Away Days: We offer 20 company-wide paid days off—which help our teams collectively pause to recharge
- Learning & Development: Engage in exciting learning programs to level up your growth
Company HQ
Chinatown, San Francisco, CA
Leadership
Max Levchin
(Co-Founder & CEO)Has founded/co-founded numerous companies, holding various executive positions at each, including Paypal, Slide, Glow, HVF Labs, and SciFi VC. Also worked at large brands including Google, Yelp, Yahoo, and Evernote.
Share this job
View 111 more jobs at Affirm