Remote opportunity at
AffirmSenior Software Engineer, Backend (ML Feature Platform)
Affirm is seeking a Senior Software Engineer, Backend for its ML Feature Platform team to design and scale tools that power machine learning and decisioning systems…
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About This Role
Affirm is seeking a Senior Software Engineer, Backend for its ML Feature Platform team to design and scale tools that power machine learning and decisioning systems across the company. Affirm provides transparent financial services that allow people to pay over time without hidden fees. In this fully remote role, the engineer will own end-to-end feature flows, storage infrastructure, and offline backfilling platforms to support machine learning engineers, data analysts, and…
Job Description
Affirm is seeking a Senior Software Engineer, Backend for its ML Feature Platform team to design and scale tools that power machine learning and decisioning systems across the company. Affirm provides transparent financial services that allow people to pay over time without hidden fees. In this fully remote role, the engineer will own end-to-end feature flows, storage infrastructure, and offline backfilling platforms to support machine learning engineers, data analysts, and decisioning teams.
This position involves leading technical initiatives, delivering quarterly team objectives, navigating ambiguous technical challenges, and setting code review and design standards. The ideal candidate will work closely with product management, design, and analytics partners to balance technical constraints and risks while driving high availability and low latency across core systems.
Responsibilities
- Own and deliver quarterly goals for the ML Feature Platform team
- Guide engineers through open-ended problems and ambiguity to ensure project delivery
- Collaborate with product management, design, and analytics stakeholders on the product development lifecycle
- Identify and resolve technical, process, technology, and business issues
- Monitor metrics, handle escalations, and support on-call operations and system availability
- Establish code review and design standards while mentoring team members
- Contribute to technical writing and tech talks to advocate for engineering quality
Requirements
- Have at least four years of experience designing, building, and launching distributed backend systems at scale
- Demonstrate experience with programming languages such as Python or Kotlin
- Show a track record of building highly available distributed systems utilizing technologies like AWS, MySQL, and Kubernetes
- Possess experience creating technical plans for significant features or system components with simple and extensible designs
- Demonstrate proficiency in safely making significant changes to large code bases using established tools and practices
Qualifications
- Hold a Bachelor’s degree in a related field or possess equivalent practical experience
Core Skills
Benefits
- 100% subsidized medical coverage, dental, and vision for employees and their dependents
- Monthly spending stipends for health, wellness, and technology setup
- Flexible time off and generous holiday calendars
- Employee stock purchase plan to purchase company stock at a discount
Frequently Asked Questions
Answers are based only on the employer’s listing; where it doesn’t say, neither do we.
Is this position remote?
Yes, this is a remote-first position where the role can be performed from almost anywhere within the country of employment, though new hires are invited to an in-person onboarding experience.
What is the salary range for this role?
For candidates located in California, Washington, New York, New Jersey, and Connecticut, the annual base pay ranges from $195,000 to $225,000. For all other U.S. states, the base pay ranges from $173,000 to $233,000.
What background is required for this role?
Candidates must have four or more years of experience building scalable backend systems using Python or Kotlin, along with experience using AWS, MySQL, and Kubernetes for distributed systems.
Sample Interview Questions
AI-generated questions tailored to this specific role — a preview of the full practice set.