Senior Machine Learning Engineer - Venues
Since our inception in 2009, Foursquare has been a leading force in changing how location information enriches our real-world and digital lives. As a location intelligence company, Foursquare is comprised of two well-known consumer apps, Foursquare and Swarm, as well as thriving media and enterprise products. Our B2B offerings include Places (for developers), Pinpoint and Attribution (for marketers), and Place Insights (for analysts, based on the world's largest foot traffic panel). With more than 300 people across our offices in New York, San Francisco, and in sales offices around the globe, we’re dedicated to our trailblazing mission—to build the most trusted, independent platform for understanding how people move through the real world.
About our Engineering Team:
As a member of Foursquare’s engineering team, we want you to bring experience building real products from the ground up. We're passionate about tackling tough challenges in the location space and look for others who like to dive deep into code and help solve hard problems. You should be comfortable running with your own ideas and eager to learn new skills on a bleeding edge platform. We use a variety of tools, technologies, and languages to build software (Scala, Python, Spark, EMR, MongoDB, Pants, Thrift) but experience with equivalent ones will do just fine.
Join us and help bring our feature ideas (and your own!) off the whiteboard and into reality. As a Machine Learning Engineer, you will research improvements in data collection, feature engineering, and algorithmic optimization. You will also work on implementing your models in production systems and data pipelines. Here are some high-level applications of machine learning at Foursquare that you could work on within our NY office:
- Expanding on methods to learn from aggregated user activity data at scale with a variety of big data ML applications
- Investigating ways to improve the third dimension for location intelligence through feature engineering and incorporation of signals that go beyond GPS and WiFi
- Using NLP techniques to normalize, and infer structure from, unstructured place data from disparate sources
- Entity resolution and deduplication across of hundreds of millions of place records from providers
- Extracting the freshest and most correct information about a real-world place given data from publishers of varying quality
- Performing causal modeling and turning model outputs into real, actionable insights on a product that builds hundreds of machine learning models per day at scale to drive marketing decisions for many well-known companies and brands
- Masters (preferred PhD degree) in Computer Science or related technical field or equivalent practical experience
- 2+ years of work or educational experience in Machine Learning.
- Strong knowledge of ML techniques including both supervised and unsupervised learning, feature engineering, classification, regression, and optimization
- Proficiency in statistics
- Experience working with large, complex and diverse data sets from a variety of sources
- Ability to collaborate with a diverse set of engineers and data scientists
- Experience with one or more general purpose programming languages including but not limited to: Java, Python, Scala or C/C++
- Experience with Hadoop, scalding, Databricks, spark, EMR or similar framework a plus
Foursquare is proud to foster an inclusive environment that is free from discrimination. We strongly believe in order to build the best products, we need a diversity of perspectives and backgrounds. This leads to a more delightful experience for our users and team members. We value listening to every voice and we encourage everyone to come be a part of building a company and products we love.
Foursquare is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected Veteran status, or any other characteristic protected by law.