Principal Software Engineer, Machine Learning Simulations

Upstart is a leading AI lending marketplace focused on reducing the cost and complexity of borrowing for all Americans. The Principal Software Engineer will be responsible for building an MLOps platform to support machine learning model inference and process automation, while also contributing to a marketplace simulation platform for innovation across teams.

Responsibilities:

• Build, maintain, and optimize Upstart’s next-generation machine learning and simulation platform, enabling increased scale, performance, and confidence in decisioning
• Develop high-quality software applications that enable machine learning models to be applied to the ever-evolving needs of the business
• Enable the modernization of our serving infrastructure, reducing inference latency to just a few seconds for our most complex models
• Design and contribute to our simulation systems to more accurately reflect production environments, reducing simulation cost and enabling broader usage across teams
• Communicate closely with cross-functional partners from ML, Engineering, Product, and Data Engineering teams, keeping all stakeholders informed
• Mentor engineers across the team, sharing expertise on distributed systems, MLOps, and scalable architecture

Requirements:

• 10+ years of software engineering experience, including experience building and contributing to machine learning platforms, ML infrastructure, or adjacent large-scale data or model-serving systems
• Experience building or contributing to platforms or systems that support machine learning model simulation
• Experience building self-serve or configuration-driven tooling for internal stakeholders
• Experience building and maintaining backend software services and APIs
• Proficiency with some or more of the following: Python, Kotlin, Databricks, and AWS
• Exhibits a growth mindset - you're not afraid to pick up new technologies that are best for the task, and learn from others
• Ability to quickly comprehend and reiterate complex requirements from product or engineering leadership and translate those to both technical and non-technical stakeholders
• Track record of successfully mentoring and developing other engineers around you while seeking out and appreciating constructive feedback
• Familiarity with model serving technologies like Ray, and experimentation frameworks
• Proficiency with Flask, FastAPI, Metaflow, MLflow, gRPC, Kafka, Spark/PySpark, ETL/ELT, Redshift (or similar)
• Excellent quantitative reasoning skills with interest in working at the intersection of engineering and machine learning
• Strong sense of ownership and accountability for the quality and timely delivery of work
• Proven ability to effectively analyze and solve complex problems
• Excellent written and verbal communication skills with stakeholders, peers and product owners
• Ability to thrive both in self-directed work environments and in collaborative settings, contributing positively to team dynamic