Senior Engineering Manager, Marketplace

Upstart’s Marketplace Optimization team is responsible for optimizing market-level outcomes, balancing tradeoffs for borrowers, capital providers, and Upstart. The team builds the dynamic policy levers, matching and offer-selection logic, and monetization tooling that clear the marketplace, manage margins, and maximize capital utilization efficiency.

Our mandate is to enable automated, data-driven decisions that improve originations, contribution margin, and capital efficiency while supporting new products and partner integrations.

As the Senior Engineering Manager on the team for Marketplace Optimization, you will lead high-impact engineering squads that turn marketplace strategy and ML-backed signals into reliable production systems. You’ll partner closely with Product, Machine Learning, and Capital Markets to deliver scalable capital routing, matching, and monetization automation capabilities that directly affect borrower outcomes and capital performance.

You will set execution priorities, remove blockers, and develop engineers to ship production-grade services that are observable, performant, and safe for a regulated fintech environment.

• Define and execute the team’s technical roadmap : deliver matching, offer-selection, monetization, and target-return logic and features that improve marketplace outcomes

• Build and grow a high-performing engineering team : hire, mentor, and develop engineers and ICs; establish clear expectations and career growth paths

• Drive technical excellence : own architecture and delivery decisions for scalable, fault-tolerant services, data pipelines, and APIs that integrate with ML models and capital systems

• Collaborate cross-functionally with Product, ML, Capital Markets, and Risk to translate business goals into prioritized, measurable engineering work

• Own operational reliability and observability : establish SLOs/SLIs, alerting, runbooks, and post-incident practices to ensure marketplace integrity and fast incident response

• Lead delivery and execution : unblock teams, manage technical and organizational risks, and ensure high-quality, timely releases while balancing speed and safety

• Bachelor’s degree in Computer Science, Engineering, or Mathematics, or a related field (or its equivalent) + 8 years of experience, including at least 3 years of direct people management experience.

• Proven success leading and scaling high-performing engineering teams (e.g., growing headcount, improving delivery metrics, or managing multiple squads)

• Demonstrated domain expertise in marketplace optimization, pricing/fee/monetization, or routing systems at scale

• Demonstrated ability to partner closely with Product, and ML teams to deploy models at scale and build scalable production ready systems

• Exceptional communication skills with the ability to influence technical and non-technical audiences

• Strong analytical, organizational, and strategic thinking skills with a bias for action

• Hands-on familiarity with cloud platforms (AWS/GCP/Azure), containerization and orchestration (Docker/Kubernetes), and scalable API design

• Experience with streaming and event architectures (e.g., Kafka, Pub/Sub) and modern data platforms

• Track record operating in regulated or compliance-sensitive environments and building systems that support auditability

• Background in statistics, economics, or applied ML domains

• Demonstrated success building observability and reliability practices (SLO/SLI design, monitoring, alerting, post-incident process) and scaling engineering processes across multiple teams

Position Location - This role is available in the following locations: Remote, San Mateo, Columbus, Austin

Time Zone

Requirements - This team operates across all U.S. time zones.

Travel

Requirements - As a digital first company, the majority of your work can be accomplished remotely. The majority of our employees can live and work anywhere in the U.S but are encouraged to to still spend high quality time in-person collaborating via regular onsites. The in-person sessions’ cadence varies depending on the team and role; most teams meet once or twice per quarter for 2-4 consecutive days at a time.