Software Engineer II, Marketplace Optimization

Role overview Build and operate backend systems that support real-time decisions in a two-sided financial marketplace. The work covers borrower-to-capital matching, offer selection, experimentation, and monetization; engineering choices can affect marketplace efficiency, capital use, reliability, and customer experience.

Responsibilities - Design, build, and operate scalable services for real-time marketplace decisioning, matching, offer selection, and monetization. - Create platform capabilities that support experimentation with allocation strategies and marketplace economics. - Maintain dependable, observable systems with appropriate attention to latency, availability, correctness, and operational simplicity. - Work with product, machine learning, pricing, data science, capital markets, and lending partnership teams to turn business needs into technical solutions. - Simplify architecture, reduce coupling between systems, and develop reusable capabilities for future marketplace needs. - Use generative AI tools where useful in development while retaining responsibility for security, quality, testing, and technical decisions.

Requirements - A degree in computer science, engineering, mathematics, or a related area, or equivalent experience, and at least two years of software engineering experience. - Production experience designing, building, and operating backend or distributed systems. - Experience developing production services in one or more modern backend languages. - Experience supporting business-critical services through testing, monitoring, incident response, and root-cause analysis. - Understanding of reliability, availability, observability, and correctness in production system design. - Ability to work through ambiguous technical and business problems and collaborate across functions.

Nice to have - Experience with marketplaces, financial systems, recommendation or matching engines, optimization, experimentation platforms, or low-latency, high-throughput systems. - Experience integrating machine learning models or data-driven decisioning into production. - Familiarity with Ruby, Ruby on Rails, Python, or Kotlin.

Benefits and work setup - Remote from the United States or Canada, excluding Quebec. Teams commonly hold in-person sessions once or twice per quarter for several consecutive days; travel cadence varies by role. - Benefits include health coverage, retirement plans, paid leave, family support, wellness and productivity allowances, and equity-related programs, subject to eligibility and location.