Sr. Software Engineer, Machine Learning Infrastructure

About Us

Pursuit of truth in credit. 

By using machine learning to anticipate and manage risk in credit, we’re empowering our partners and lenders to unlock opportunity and access for more borrowers, everywhere. 

We strive to be the preferred partner to lending platforms, providing not only access to capital but also underwriting technology capabilities to allow innovative lending platforms to grow their business. 

Our firm is made up of 60+ professionals working in San Mateo (HQ) and New York, working in-office on Tuesdays and Thursdays. We are passionate, hard-working, relentlessly-resourceful, impact-focused individuals. We deeply value intellectual curiosity, independence of thought, creative idea generation, empathy, and close collaboration. 

What You'll Do

As a part of Theorem’s technology team, you will work alongside quantitative researchers, data scientists, and engineers to build and maintain the infrastructure which drives Theorem’s machine learning-based credit underwriting systems. You’ll develop software systems for both training and serving models at scale. You will work on production systems that underwrite real loans as well as on experimental systems that researchers use to test new ideas and models. 

Here are some examples of the types of problems you’ll be working on:

• Creating automation for retraining and backtesting models upon the arrival of new data
• Building CI/CD automation for redeploying models into staging environments
• Creating dashboards for analyzing production model performance
• Integrating model training pipelines into cloud monitoring and alerting systems
• Responding to production incidents that involve model training & serving systems
• Integrating model training and serving systems into Theorem’s data warehouse
• Building highly scalable model training infrastructure that can leverage multiple machines and GPU hardware
• Incorporating new machine learning frameworks and tools into Theorem’s software stack
• Evaluating and deploying open-source tools for workflow orchestration, data lineage, experiment/metadata tracking, etc.

What We're Looking For

• Bachelor's degree in computer science, engineering, mathematics, or a related technical field
• Minimum of 4 years of professional software development experience
• Understanding of how to build, deploy, debug, and operate distributed systems that run in a containerized cloud environment
• Experience with cloud environments (AWS preferred) & container orchestration tools (ie. K8s, YARN, etc.)
• Experience with instrumenting cloud-based systems with metrics & developing monitoring to alert to production incidents (Prometheus/Grafana, Datadog, etc.)
• Working knowledge of the Python programming language (including scientific Python tools such as Pandas, NumPy, scikit-learn, etc.)

Bonus

• Previous experience in training and serving machine learning models, focusing on infrastructure (rather than model development or model evaluation itself)
• Experience developing high-scale distributed systems
• Experience with Helm and/or Kustomize
• High level knowledge of common machine learning techniques (e.g. decision trees, SVMs, neural networks, etc.)

Our Commitment

We foster an environment that welcomes professionals with a diversity of backgrounds and ideas. We value professionals who are thoughtful, innovative, tenacious, and mission-driven. Every member of the team has a major impact on the company's success with visible contributions to the business. We encourage and reward growth, learning, and a solutions-seeking mindset. We offer a competitive salary and opportunity for equity ownership, generous benefits, and an inclusive and collaborative work environment. If you’re excited by the opportunities to create outsized impact as part of a world-class team, we strongly encourage you to apply.

We provide reasonable accommodation for qualified individuals with disabilities and disabled veterans in job application procedures.