Software Infrastructure Engineer

San Francisco, CA, United States

Theorem


Role Location

  • San Francisco, CA, United States

Employees

11 - 25 people

Address

436 Bryant St
San Francisco, CA, 94107, US

Tech Stack

  • Pandas
  • Numpy
  • Scikit Learn
  • Data Science
  • Python
  • C++
  • Statistics
  • Machine Learning
  • Fast Code

Role Description

Key responsibilities: Build and maintain Theorem’s continuous software delivery pipeline, empowering our research and engineering teams to land impactful changes more quickly and with greater confidence Design and implement comprehensive system integration tests that exercise our software components exactly as if they were deployed in our live environment, reducing the risk of releasing new software into production Organize, maintain, and extend our data warehousing infrastructure, enabling our researchers to conduct sophisticated ad-hoc analyses and experiments at scale Integrate our software systems and data pipelines throughout Theorem to automate away toil of legacy business processes Develop system dashboards and tools for monitoring Theorem’s production environment and facilitate effective incident responses to unexpected events Establish processes and procedures for operating production systems and helping Theorem strive to become a world-class software development organization

Experience and Knowledge required: Proficiency with both statically-typed as well as dynamically-typed programming languages Experience with operating software on cloud platforms such as AWS or GCPFamiliarity with cutting-edge CNCF technologies, such as Kubernetes, Prometheus, and gRPC Understanding of big-data processing frameworks like Apache Hadoop or Spark Expertise with data warehousing technologies such as Amazon Redshift and Google BigQuery also highly desirable Operational experience with automated monitoring and alerting, on-call rotation participation, incident response, and process automation highly appreciated

About Theorem

We are a cross-disciplinary team applying machine learning and rigorous scientific investigation to revamp the lending and securitization space. This is one of finance’s least sexy areas, but is a multi-trillion dollar market- and it’s where the financial crisis started. Bad technology was a major cause, and even after almost 10 years, no one has fixed it.

Company Culture

Smart, friendly and kind. Hardworking, respectful. Eagerly nerdy, collaborative, research and science obsessed.

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