Infrastructure Engineer - CU105
San Francisco, CA

Job Description

CircleUp harnesses the power of machine learning and predictive analytics to discover some of the fastest-growing companies in the consumer & retail sector. Our mission is to help entrepreneurs thrive by giving them the resources and capital they need. We are building a predictive data system called "Helio" to bring the data-driven revolution that has occurred in the public markets to the private markets, starting with consumer & retail.

We are working on challenging problems in information retrieval, entity resolution, and machine learning. We are developing an in-depth knowledge graph of all private companies by mining vast amounts of data to successfully rewrite the rules on how private companies are evaluated.

CircleUp has been named one of the Top 5 Most Disruptive Companies in Finance by CNBC, one of the 50 Best Fintech Innovators by KPMG, and one of America's Most Promising Companies by Forbes. We are backed by top-tier investors including Google Ventures, Union Square Ventures (backers of Etsy/Kickstarter), and the ex CEOs/Presidents of Goldman Sachs, Morgan Stanley, Thomson Reuters, the Stanford Endowment and Capital One.

We're adding talented teammates to our Engineering team. You will be working closely with other engineers, data scientists, product managers, and investment professionals to build out the capabilities of Helio.

Responsibilities:

Build performant and reliable data pipeline infrastructure in service of machine learning and predictive analytics.

Build developer tooling to enable efficient development on machine learning and data platform applications

Build automation tools and big data infrastructure to operate and configure production clusters, data orchestration, and data collection services.

Requirements:

Have a B.S., M.S. or Ph.D. in Computer Science or equivalent degree and work experience

Excellent software engineering skills and strong fundamentals in algorithms, data structures, predictive modeling and big data concepts

Strong experience with the Amazon Web Services ecosystem, particularly the tooling around data processing (EC2, S3, EMR, RDS)

Experience with other elements of our stack (Python, Pandas, Sci-kit Learn, PySpark, Airflow, Kubernetes) is preferred but not required