Why This Job is Featured on The SaaS Jobs
This Machine Learning Engineer II role sits at a mature, high-scale SaaS platform where advertising is a core monetization engine. Forecasting ad inventory and demand is a distinctly SaaS problem because it connects product usage patterns, marketplace dynamics, and revenue planning, all under real-world constraints like latency, reliability, and shifting user behavior. The remit spans models and the systems that operationalize them, reflecting how modern SaaS companies treat ML as production infrastructure rather than isolated research.
From a career standpoint, the work builds durable SaaS competencies in experimentation, measurement, and decision support. Owning forecasting systems develops judgment around data quality, model monitoring, and how predictive outputs are consumed by product and business stakeholders. Experience with cloud-native services, distributed pipelines, and microservice-oriented delivery also transfers well across SaaS organizations that run large-scale analytics and personalization workloads.
The role tends to fit engineers who enjoy ambiguity and iteration, and who want their ML work to be evaluated through platform outcomes rather than offline metrics alone. It also suits professionals comfortable collaborating across engineering, product, and business functions, particularly those interested in the intersection of applied ML and revenue-critical systems.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
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 Machine Learning Engineer with expertise in machine learning model development, AI engineering, online experimentation techniques, and large-scale engineering systems. This role will lead strategic initiatives and projects within Ads Forecasting.
The Ads Forecasting squad focuses on building and maintaining the models and systems that predict future ad inventory,demand, and performance across Spotify's platform. By leveraging data and experimentation, we aim to provide accurate, timely forecasts that drive key business decisions, optimize ad delivery, and ensure the long-term health of our advertising business. We operate on 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 someone who is motivated by user and business problems as much as they are by technical problems, and who enjoys ambiguity, brainstorming, experimentation, and iteration. You will work in close collaboration with key stakeholders across engineering, product, business, and leadership teams to build the most impactful solutions for our Spotify listeners and business partners.
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What You'll Do- 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
Who You Are- 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.
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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.
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.
Spotify transformed music listening forever when we launched in 2008. Our mission is to unlock the potential of human creativity by giving a million creative artists the opportunity to live off their art and billions of fans the chance to enjoy and be passionate about these creators. Everything we do is driven by our love for music and podcasting. Today, we are the world’s most popular audio streaming subscription service.
