Principal Software Engineer, Machine Learning Simulations

Upstart · Fintech · Remote · Engineering

Principal Software Engineer to build and operate an MLOps platform for ML model inference, process automation, model deployment, and observability, as well as a marketplace simulation platform for ML and Finance teams at Upstart, a leading AI lending marketplace.

What you'd actually do

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

Skills

Required

  • 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

Nice to have

  • 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

What the JD emphasized

  • Machine Learning Simulations
  • MLOps platform
  • machine learning model inference
  • model deployment
  • observability
  • marketplace simulation platform
  • low-latency inference
  • simulation systems
  • ML
  • Finance

Other signals

  • MLOps platform
  • model inference
  • marketplace simulation
  • low-latency inference
  • serving infrastructure