Machine Learning Engineer

San Francisco

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 pipelines in service of machine learning and predictive analytics.
  • Build our Machine Learning infrastructure, including model productization, model training, model simplification, and maintenance of existing models.
  • Build capabilities that increase the momentum of the Data Science team. Remove Engineering roadblocks and ensure the Data Science team has the tools needed to succeed.

Requirements:

  • Have a B.S., M.S. or Ph.D. in Computer Science or equivalent degree and work experience
  • Baseline understanding of the statistics behind Machine Learning algorithms
  • Excellent software engineering skills and strong fundamentals in algorithms, data structures, predictive modeling and big data concepts
  • Experience with our stack (Python, Pandas, Sci-kit Learn, PySpark, Airflow, AWS ecosystem) is preferred but not required
  • Experience with Entity Resolution or Knowledge Graph problem spaces is a huge plus
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