Full Stack Software Engineer, Machine Learning

Posted on Dec 11, 2018 by Workday

Boulder, CO 80301
Immediate Start
Annual Salary

Join our team and experience Workday!

It's fun to work in a company where people truly believe in what they're doing. At Workday, we're committed to bringing passion and customer focus to the business of enterprise applications. We work hard, and we're serious about what we do. But we like to have a good time, too. In fact, we run our company with that principle in mind every day: One of our core values is fun.

Job Description

The HCM (Human Capital Management) Machine Learning (ML) Product Engineering team is looking for strong, seasoned full stack software engineers to help expand our product offering of activated machine learning products at Workday.

Our team of ML software engineers, systems engineers, data scientists and product managers are building products leveraging modern approaches in Natural Language Processing (NLP), geometric search/information retrieval, and Artificial Intelligence to activate the right product offering.

Our Full Stack Engineers build UI frameworks, highly available service layers, and help in streamlining large scale recurring ML ETL tasks. We expect all our engineers to have "product sense" and incredible creativity as percolation of different viewpoints across the stack help us evolve a product to a more optimal state.

Familiarity with ElasticSearch, Redis, PostgresSQL/MySQL, RabbitMQ is helpful and any knowledge of ML/NLP related toolkits are a plus, e.g., tensorflow, sklearn, spaCy, etc. As UI/UX as general functionality of an experience on top of ML activated products is where the "rubber hits the road", this is a very important intersection point of customers interacting with ML driven applications.

Knowledge of React, Python, Scala, CSS and UX are helpful in bringing the face to these machine learning products to life.

We're looking for individuals with at least three years of experience in this part of the stack and those who are activated by a product empiricist paradigm of iteration given customer feedback.

Reference: 597317966

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