Senior Software Engineer - Machine Learning Platform

Upstart is hiring a Senior Software Engineer for its Machine Learning and Simulations Platform team. The role builds and operates MLOps and simulation infrastructure for model inference, deployment, feature management, and observability, while enabling rapid innovation across machine learning and finance 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
• Build self-service tooling so ML teams can register features and deploy models independently, and reduce the manual work the platform team absorbs today
• Deliver the data and feature infrastructure behind every model, including feature definition, storage, serving, and offline to online parity
• 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:

• 6+ years of software engineering experience
• Experience building and maintaining backend software services and APIs
• Experience with distributed systems or large scale data processing, using Spark, Databricks, Ray, or an equivalent
• Experience with an ML platform or the ML production path, such as training pipelines, model serving, feature pipelines, or a training data platform
• Proficiency with some or many of the following: Python, Kotlin, Databricks, and AWS
• Exhibits a growth mindset
• You pick up new technologies that fit the task, and you learn from others
• Ability to quickly comprehend complex requirements from ML, product, or engineering leadership, and translate them for both technical and non-technical partners
• Skill with Metaflow, MLflow, gRPC, Spark/PySpark, dbt, Ray, GPU
• Knowledge of simulation, experimentation, or backtesting systems
• Experience building self-serve or configuration driven tooling for internal users
• 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
• Excellent written and verbal communication skills with partners, peers, and product owners
• Ability to thrive in self-directed work and in collaborative settings, contributing positively to team dynamics