Staff Machine Learning Engineer, Home Surfaces

What You'll Do

Who You Are

• You have deep experience building and deploying machine learning systems in production environments.
• You have deep experience with recommendation systems, ranking models, personalization, or large-scale content discovery platforms.
• You are proficient in Python and have hands-on experience building machine learning systems with PyTorch.
• You have experience with large language model training, fine-tuning, evaluation, and optimization techniques, including SFT, distillation, and LoRA.
• You have worked with large-scale inference systems and understand the challenges of latency, reliability, and cost optimization.
• You care about creating high-quality user experiences through thoughtful applications of machine learning.
• You communicate effectively with technical and non-technical partners and can influence technical decisions beyond your immediate team.
• You know how to design, execute, and interpret online experiments and A/B tests to improve user outcomes.
• You have experience operating distributed machine learning workloads using technologies such as Ray, FSDP, HSDP, or similar frameworks.
• You have experience building and maintaining data pipelines and orchestration workflows using technologies such as Flyte, Airflow, BigQuery, and cloud-based storage platforms.
• Where You'll Be
• We offer you the flexibility to work where you work best! For this role, you can be within the North Americas region as long as we have a work location.
• This team operates within the Eastern Standard time zone for collaboration.
• The United States base range for this position is $227,495- $324,993 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. 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.

Where You'll Be

Responsibilities

• Own and improve the machine learning models and systems that power the Home feed, including the Shortcuts experience.
• Design, build, and ship personalized recommendations that serve millions of Spotify listeners globally.
• Build content recommendation systems for emerging agentic and AI-powered user experiences.
• Train, fine-tune, evaluate, and optimize large language models using techniques such as supervised fine-tuning (SFT), distillation, and parameter-efficient training approaches.
• Partner closely with Product Managers, Engineers, Data Scientists, and Designers to define and execute experimentation strategies.
• Drive A/B testing, monitoring, model evaluation, and continuous optimization of recommendation quality, reliability, and cost efficiency.
• Improve ML platform capabilities, data pipelines, and production systems that support personalization at Spotify scale.
• Drive technical direction in ambiguous problem spaces and contribute to the long-term architecture of our personalization systems.
• Mentor and support other Machine Learning Engineers, sharing knowledge and helping strengthen engineering practices across the team.