Machine Learning Engineer
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Requirements
• You have professional experience in applied machine learning • You have strong technical expertise in application development, microservice architecture, distributed systems and/or data analysis • You are proficient in programming languages such as Python, Java, or Scala • You are skilled with operating in a cloud-native infrastructure • You have experience in developing data pipelines using tools like Apache Beam or Spark • As a plus, you may have experience with adtech, categorization systems, and evaluation tools / data curation techniques • Where You'll Be • We offer you the flexibility to work where you work best! For this role, you can be within the Americas region as long as we have a work location. • This team operates within the Eastern time zone for collaboration. • The United States base range for this position is $148,901.00 - $212,716.00, plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave. This range encompasses multiple levels. Leveling is determined during the interview process. Placement in a level depends on relevant work history and interview performance. These ranges may be modified in the future. • At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone. We have ways to request reasonable accommodations during the interview process and help assist in what you need. If you need accommodations at any stage of the application or interview process, please let us know - we’re here to support you in any way we can.
Responsibilities
• Design and implement machine learning systems to predict future ad inventory,demand, and performance • Research and apply best practices for driving automation with respect to human review processes • Partner with multiple teams to shape and enhance shared systems and pipelines • Come up with creative ways to apply AI tools to develop innovative solutions • Collaborate with and lead backend engineers, data scientists, data engineers, and product managers to establish baselines, inform product decisions, and develop new technologies