Director of Engineering, Machine Learning and Simulations Platform

About Upstart

Upstart is the leading AI lending marketplace partnering with banks and credit unions to expand access to affordable credit. By leveraging Upstart's AI marketplace, Upstart-powered banks and credit unions can have higher approval rates and lower loss rates across races, ages, and genders, while simultaneously delivering the exceptional digital-first lending experience their customers demand. More than 80% of borrowers are approved instantly, with zero documentation to upload.

Upstart is a digital-first company, which means that most Upstarters live and work anywhere in the United States. However, we also have offices in San Mateo, California;
Columbus, Ohio; and Austin, Texas.

Most Upstarters join us because they connect with our mission of enabling access to effortless credit based on true risk. If you are energized by the impact you can make at Upstart, we’d love to hear from you!

The Machine Learning and Simulations Platform (MLSP) team builds and operates the core infrastructure that powers ML model training, inference, and marketplace simulation  platform is foundational to the company’s success—every underwriting, fraud, conversion, and verification model runs here. We also provide the simulation capabilities that help teams experiment safely and assess business impact without requiring costly live experimentation.

We are on a mission to reimagine our infrastructure to support the growing complexity of our ML models, the demand for low-latency inference, and the accuracy needed to simulate the dynamics of our borrower-lender marketplace  team partners closely with Engineering, ML, Product, and Compliance to accelerate innovation while safeguarding performance and integrity.

• Drive the technical vision and roadmap for Upstart’s next-generation machine learning and simulation platform, enabling increased scale, performance, and confidence in decisioning.
• Lead efforts to modernize our model training and serving infrastructure, reducing training time from days to hours and inference latency to just a few seconds for our most complex models.
• Spearhead the design and implementation of a distributed platform capable of supporting neural networks, GPU-based training, and scalable hosting of complex models.
• Overhaul our simulation systems to more accurately reflect production environments, reducing simulation cost and enabling broader usage across teams.
• Work cross-functionally with ML, Engineering, Product, and Finance leaders to align on long-term strategy and incrementally deliver measurable business value.
• Build and mentor a team of senior engineers and technical leads (including existing leaders), enabling them to grow and scale their areas of ownership.
• Identify opportunities to expand the charter and unlock new investment areas, such as automated model refreshing and simulation-driven product development

• Minimum requirements:
  • 15+ years of experience in software engineering, including 5+ years leading teams or organizations focused on ML infrastructure, simulations, or large-scale distributed systems.
  • Proven track record of launching science-heavy products and ML infrastructure at scale, including experience with model training, serving, and performance optimization.
  • Strong systems and infrastructure fundamentals, with the ability to design and evaluate architectures for high-throughput and low-latency ML workloads.
  • Ability to collaborate effectively across disciplines and communicate complex technical concepts to non-technical stakeholders.
  • Experience managing senior engineers and driving alignment across multiple cross-functional teams.
• Preferred qualifications:
  • Experience partnering directly with Research and Compliance teams to bring regulated or science-heavy products to market.
  • Familiarity with simulation platforms, experimentation frameworks, or financial modeling tools used to assess impact offline.
  • Deep knowledge of modern ML tooling, including GPU-based training, neural network frameworks, and containerized infrastructure.
  • Demonstrated ability to scale engineering organizations and expand platform charters through influence and…