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.
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.
Who We Are
The Shopping Core ML team’s mission is to deliver state of the art ML algorithms that will help build robust and scalable customer products. We use a data-driven approach to solve the problems and deliver ML-powered smart user experiences on the consumer apps and website. This team works in close partnership with our product managers, application and website teams to deeply understand the problems and deliver the most impactful solutions. We are constantly faced with challenges at industrial scale such as Personalization, Information Retrieval, Relevance Ranking and are pushing the boundary of how Affirm thinks of its data.
We are looking for highly motivated software engineers to help build this team. Come join us!
What You’ll Do
- Partner with Data Science and Product engineering teams to build production machine learning models; your models will decide who we lend to in real time, and how we interact with our existing customers
- Develop the core decisioning service supporting our machine learning models in production
- Optimize the decisioning service with respect to the accuracy of the underlying models, the latency of different 3rd party data sources, and the expected financial cost of those data sources
- Develop our understanding of new data sources and how they may improve our existing processes
- Design, develop, and deploy infrastructure for the training, testing, and serving of models at scale
- Work closely with Affirm’s credit, risk, and analytics teams to understand our risk modeling strategy and the business drivers underlying that strategy
What We Look For
- B.S. with 5+ years of industry experience, M.S. with 4+ years, PhD with 3+ years, or equivalent experience.
- Deep understanding and experience with data engineering, data analysis, and statistical modeling, in the development of real-time services
- Proficiency writing production-quality software following software engineering best practices, preferably industry engineering experience with machine learning projects (e.g. recommendation, ranking, optimization)
- Strong foundation in machine learning with experience implementing and optimizing machine learning models
- Hands on experience with Python, Spark, XGBoost, Airflow, and AWS (EC2, EMR, etc.) a plus
- Ability to work efficiently both solo and as part of a team; willingness to learn new things
- Passion and drive to change consumer banking for the better, while developing a deeper understanding of applied machine learning
Compensation & Benefits
We offer a competitive package, with some highlights listed below. However, the given figures are not guaranteed compensation ranges; rather, they are unbinding, approximate indications of what the salary may be for your awareness. The actual salary may be less than the lower range or greater than the upper range, depending on skills and experience. No employee is guaranteed salary at the amount of the lower range.
- Targeted Gross Monthly Salary: 22,040 - 27,550 PLN
- Type of employment: Contract of Employment
- Flexible Spending Wallets for tech, food and lifestyle
- Generous time off policies
- Away Days - wellness days to take off work and recharge
- Learning & Development programs
- Parental leave
- Robust health benefits
- Employee Resource & Community Groups
- This role is eligible for creative tax benefits, subject to applicable law and company policy
Location - Remote Poland
The majority of our roles can be located anywhere in Poland.
**This job description is not a contractual document, and is not intended to have binding force.**
#LI-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 have a simple and transparent remote-first grade-based compensation structure. Offer amounts within the range are based on a number of factors including but not limited to job-related skills, experience, and relevant education or training. Across the broader organization, certain roles are eligible for equity awards upon hire, promotion, tenure milestones and for performance.
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.
By clicking Submit Application, you acknowledge that you have read the Affirm Employment Privacy Policy for applicants within the United States, the EU Employee Notice Regarding Use of Personal Data (Poland) for applicants applying from Poland, the EU Employee Notice Regarding Use of Personal Data (Spain) for applicants applying from Spain, or the Affirm U.K. Limited Employee Notice Regarding Use of Personal Data for applicants applying from the United Kingdom, and hereby freely and unambiguously give informed consent to the collection, processing, use, and storage of your personal information as described therein.
