Machine Learning Engineer, Ad Forecasting

About the Role: Machine Learning Engineer, Ad Forecasting at Spotify

Our mission on the Advertising Product & Technology team at Spotify is to build a next-generation advertising platform that aligns with our unique value proposition for audio and video. We are dedicated to scaling the user experience for hundreds of millions of fans and hundreds of thousands of advertisers. This extensive scale presents unique challenges while offering tremendous opportunities for our artists and creators.

We are seeking a Machine Learning Engineer with profound expertise in machine learning model development, AI engineering, online experimentation techniques, and large-scale engineering systems. In this critical role, you will lead strategic initiatives and projects specifically within Ads Forecasting.

The Ads Forecasting squad is focused on building and maintaining the sophisticated models and systems that predict future ad inventory, demand, and performance across Spotify's platform. By leveraging extensive data and advanced experimentation, we aim to provide accurate, timely forecasts that drive key business decisions, optimize ad delivery, and ensure the long-term health and growth of our advertising business. We operate at the cutting edge of both machine learning and AI engineering, employing both in-house first-party models developed through traditional machine learning and state-of-the-art time-series and predictive modeling techniques.

We are looking for an individual who is motivated equally by user and business problems as they are by technical challenges, and who thrives in environments characterized by ambiguity, collaborative brainstorming, rigorous experimentation, and iterative development. You will work in close collaboration with key stakeholders across engineering, product, business, and leadership teams to build the most impactful solutions for both Spotify listeners and our valued business partners.

What You'll Do

• Design and implement robust machine learning systems to accurately predict future ad inventory, demand, and performance.
• Research and apply best practices for driving automation with respect to human review processes, enhancing efficiency and accuracy.
• Partner with multiple teams across Spotify to shape and enhance shared systems and data pipelines.
• Come up with creative and innovative ways to apply advanced AI tools to develop groundbreaking solutions.
• Collaborate effectively with and provide technical leadership to backend engineers, data scientists, data engineers, and product managers to establish baselines, inform strategic product decisions, and develop new technologies.

Who You Are

• You have professional experience in applied machine learning, demonstrating a solid track record.
• You possess strong technical expertise in application development, microservice architecture, distributed systems, and/or comprehensive data analysis.
• You are proficient in programming languages such as Python, Java, or Scala.
• You are skilled and comfortable operating within a cloud-native infrastructure environment.
• You have hands-on experience in developing data pipelines using industry-standard tools like Apache Beam or Spark.
• As a plus, you may have experience with adtech, advanced 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, ensuring seamless teamwork.

The United States base salary range for this position is $148,901.00 - $212,716.00, plus equity. Spotify offers a comprehensive benefits package including health insurance, six months of paid parental leave, a 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, and paid sick leave.

Key skills/competency

• Machine Learning
• AI Engineering
• Ad Forecasting
• Time-Series Modeling
• Predictive Modeling
• Distributed Systems
• Data Pipelines
• Python/Java/Scala
• Cloud-Native Infrastructure
• Online Experimentation