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The Team: 

The Machine Learning & Simulations Platform (MLSP) team builds and operates the core infrastructure that powers ML model training, inference, and marketplace simulation at Upstart. Our 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 at scale. The team partners closely with Engineering, ML, Product, and Finance  to accelerate innovation while safeguarding performance and integrity.

As a Principal Software Engineer focused on Machine Learning Simulations at Upstart, you will be responsible for building an MLOps platform to support machine learning model inference, process automation, model deployment, and observability. Machine Learning is critical to Upstart's core business, and our greatest competitive advantage lies in the fact that we're able to innovate on our AI engine quickly. You will also help build a  marketplace simulation platform to support rapid innovation across ML and Finance teams.

How you'll make an impact

• 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

Minimum Qualifications  

• Bachelor's degree in Computer Science, Engineering, or Mathematics, or a related field (or its equivalent) + 8 years of experience
• 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

Preferred Qualifications

• 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

Position location This role is available in the following locations: Remote-US

Time zone requirements The team operates on the East/West coast 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.

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