Senior Software Engineering Manager, Upstart

• Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field, or equivalent practical experience
• 8+ years of software engineering experience, including 3+ years of engineering management experience
• Experience leading engineering teams that build and operate backend platforms or distributed systems in production
• Experience delivering platform or infrastructure programs that span multiple teams
• Proficiency with some or many of the following: Python, Kotlin, AWS, Kubernetes, Databricks, Spark

Desirable

• Upstart's Machine Learning and Simulations Platform team builds and operates the infrastructure behind ML model training, features engineering, inference, and marketplace simulation
• Our simulation platform answers one question before any change reaches a borrower: what would this new ML model, price, or fee have produced for real applicants?
• It replays historical data through production pricing logic in isolated environments, and it is now the shared tool every machine learning team at Upstart uses to size the impact of a launch
• Our priorities are reliability teams can bet a launch on, fidelity to production we can prove, cost per simulation stays in check as usage grows, and coverage that extends across the lending funnel and into new loan products
• As the Senior Engineering Manager, Machine Learning Simulations at Upstart, you will lead the team that owns this platform
• Today machine learning teams run roughly a thousand simulations a month on it
• Your job is to make it production-grade, cut its cost while usage doubles, and open it to Analytics and Finance teams who need the same answers about pricing and portfolio strategy
• You will work across machine learning, pricing, product, analytics, and infrastructure teams, many of whom own the logic your platform has to reproduce faithfully
• Own the engineering roadmap for Upstart's marketplace simulation platform, and decide which teams and use cases it serves next
• Lead, coach, and grow a team of engineers working across distributed services, offline data pipelines, and large-scale compute
• Raise simulation reliability and accuracy to a level teams trust for launch decisions, including automated checks that detect drift between simulated and production results
• Reduce the cost of each simulation as usage grows, through smart architecture and compute choices
• Extend simulation coverage across the lending funnel and into new loan products, partnering with the teams who own each stage of pricing and decisioning logic
• Turn recurring questions from machine learning, analytics, and capital markets partners into self-serve simulation workflows