Our mission on the Advertising Product & Technology team is to build a next generation advertising platform that aligns with our unique value proposition for audio and video. We work to scale the user experience for hundreds of millions of fans and hundreds of thousands of advertisers. This scale brings unique challenges as well as tremendous opportunities for our artists and creators.
We are seeking a Senior Machine Learning Engineer to join the Supply Personalization squad. Supply Personalization focuses on optimizing the volume, timing, and types of ad loads a user receives. By leveraging data, machine learning, causal inference, and large scale online experimentation, we aim to uncover and learn the most effective strategies for enhancing user experiences and driving business outcomes.
We are looking for someone with strong expertise in data analysis, online experimentation techniques, and large-scale ML and engineering systems; someone who is motivated by user and business problems as much as they are by technical problems, and who thrives under ambiguity, experimentation, and iteration. You will work directly on an array of product features that drive the optimal user experience for our ads. You will collaborate with our cross-functional teams to ideate, develop, and own complex technical solutions on our ad services technology platforms. As someone who shares our passion for building innovative ad experiences, you'll have a direct impact on how the world uses Spotify.
What You'll Do
Design and implement machine learning systems for ad performance optimization.
Research and apply ML optimization strategies to balance multiple objectives effectively.
Analyze data and use machine learning techniques to understand user behavior and improve ad experiences.
Collaborate with backend engineers, data scientists, data engineers, and product managers to establish baselines, inform product decisions, and develop new technologies.
Who You Are
You have professional experience in applied machine learning.
You have strong technical expertise in software engineering, data analysis, and machine learning.
You are proficient in programming languages such as Python, Java, or Scala.
Experienced in Tensorflow or PyTorch and working with various aspects of the ML lifecycle
You have expertise in developing data pipelines using tools like Apache Beam or Spark.
As a plus, you may have experience with any of the following - LLMs, Ray, Adtech, or Recommender Systems.
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 U.S. Eastern time zone for collaboration.
The United States base range for this position is $184,050.00 - $262,928.00, 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.
Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what’s playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It’s in our differences that we will find the power to keep revolutionizing the way the world listens.
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.
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