The Music Promotion team is building products that allow creators to promote their work to reach new audiences and create lasting connections with their fans.
The company is looking for a Machine Learning Engineer to help build systems that accurately understand the performance of promotions, providing customers with actionable insights for their promotion strategies.
As an ML Engineer, you will complete strategies for understanding the factors influencing the performance of promoted tracks globally.
You will build data-driven solutions and effective online and offline strategies to efficiently iterate and evaluate model approaches.
You will have access to a growing list of datasets, features, and ML infrastructure to continually experiment and improve the model-based approach.
Responsibilities include contributing to the design, build, evaluation, shipping, and refinement of systems that enhance Spotify’s promotional performance through hands-on ML development.
You will collaborate with a multidisciplinary team to optimize machine learning models for production use cases, ensuring efficiency, scalability, and adherence to success criteria.
You will influence technical design, architecture, and infrastructure decisions to support diverse machine learning architectures.
You will work with Data and ML Engineers to transition machine learning models from research and development into production.
You will implement and monitor model success metrics, diagnose issues, and participate in an on-call schedule to maintain production stability.
Requirements:
You must have experience implementing ML systems at scale in Java, Scala, Python, or similar languages, along with experience using ML frameworks such as TensorFlow or PyTorch.
You should understand how to transition machine learning models from research to production and be comfortable with innovative architectures.
A collaborative approach is essential, as you will work closely with research scientists, machine learning engineers, and data engineers to innovate and improve models.
You must have experience optimizing machine learning models for production use cases.
Preferably, you should have experience with data pipeline tools like Apache Beam, Scio, and cloud platforms like GCP.
Exposure to causal ML models, including counterfactuals, is preferred.
You should be familiar with crafting model success metric dashboards, diagnosing production issues, and be willing to participate in an on-call schedule to maintain performance.
Benefits:
The United States base salary range for this position is $138,250 - $197,500 plus equity.
Benefits include health insurance, six months of paid parental leave, and a 401(k) retirement plan.
You will receive a monthly meal allowance and 23 paid days off.
Additionally, there are 13 paid flexible holidays and paid sick leave.
The salary range encompasses multiple levels, with leveling determined during the interview process based on relevant work history and interview performance.
