Senior Software Engineer, Personalization & ML

Upstart

Remote Actively hiring Verified listing
Remote (San Mateo, CA, US) Posted 93 days ago Apply by Sep 1, 2026 $163,600$226,400 / year

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $179k
This role $195k
$109k most similar roles pay here $239k

This role pays more than 64% of similar roles. Most pay $142,400–$216,037 — the shaded band above. At the midpoint, this role pays about $195k versus about $179k for comparable roles.

Based on 240 similar postings.

Employer

About Upstart

Upstart is an AI lending platform that partners with banks and credit unions to expand access to affordable credit using non-traditional variables.

Upstart currently has 40 open roles on FindRole.

Listed pay typically runs $177,200–$245,400 across 40 roles with salary data.

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View all roles at Upstart

At a glance

TL;DR

As a Senior Software Engineer on Upstart's Servicing Engineering team, you will play a pivotal role in transforming borrower experiences through machine learning. Your daily responsibilities include embedding ML models into product and operational workflows to personalize servicing decisions, advancing the experimentation platform for faster iteration, and building feedback loops that enhance model performance. You’ll work closely with Product and Machine Learning teams to productionize model outputs and scale decisioning systems using resilient feature pipelines and real-time data integrations. Essential skills include hands-on experience in designing ML-powered features, contributing to experimentation systems, and maintaining data processing pipelines. Preferred qualifications involve scaling ML-driven ranking or recommendation systems, applying advanced experimentation methods, and ensuring fairness and explainability in ML models. This role demands expertise in Python, machine learning frameworks like TensorFlow or PyTorch, and proficiency with cloud platforms such as AWS or GCP.

Skills

Python Java Kubernetes Docker AWS CI/CD Terraform PostgreSQL Redis Apache Kafka Prometheus Grafana Git Jenkins MLOps Scikit-learn TensorFlow PyTorch Snowflake

What you'll do

  • Embed machine learning models into product and operational workflows to improve servicing decisions.
  • Advance experimentation platform to enable faster iteration and reliable measurement of strategy performance.
  • Design and run controlled experiments to enhance the effectiveness of personalization strategies.
  • Scale model-driven decisioning through resilient feature pipelines and real-time data integrations.
  • Define metrics and guardrails for ML-powered systems to ensure compliance and explainability.

What we're looking for

  • Bachelor’s degree in Computer Science, Engineering, or Mathematics + 4 years of experience.
  • Experience owning delivery of ML-powered features from design to production deployment and measurement.
  • Hands-on experience designing or contributing to experimentation systems for live environments.
  • Experience building and maintaining data processing pipelines supporting model-driven decisioning.
  • Led technical design decisions for distributed systems in ML-driven workflows (preferred).
  • Applied advanced experimentation methods beyond standard A/B testing (preferred).

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