# Staff Machine Learning Engineer **Company**: Spotify **Work arrangement**: remote **Experience**: staff **Job type**: full-time **Salary**: $227,495- $324,993 **Category**: Engineering **Industry**: Technology **Apply**: https://jobs.lever.co/spotify/9761af44-be41-43ca-8c01-79cdbb2aef93?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_fe3eaad3-398 ## Description The Personalization team at Spotify makes deciding what to play next easier and more enjoyable for every listener. We're behind some of Spotify's most-loved features, such as Blend and Discover Weekly. The Surfaces Moments team, a machine learning (ML) team within the Personalization Mission, focuses on creating moment-based experiences across Spotify surfaces. As a Staff Machine Learning Engineer, you will help shape the future of personalized discovery and engagement at Spotify. You'll work at the intersection of recommendation systems, large language models, and production-scale machine learning infrastructure to build experiences that delight users and drive meaningful impact. ## 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 personalization systems. - Mentor and support other machine learning engineers, helping raise the bar across the team. ## Requirements - 8+ years of experience building and deploying machine learning systems in production environments. - Deep expertise in recommendation systems, ranking models, personalization, or large-scale content discovery platforms. - Strong proficiency in Python and hands-on experience building machine learning systems with PyTorch. - Experience with large language model training, fine-tuning, evaluation, and optimization techniques including SFT, distillation, and LoRA. - Worked with large-scale inference systems and understand the challenges of latency, reliability, and cost optimization. - Care deeply about creating high-quality user experiences through thoughtful application of machine learning. - Communicate effectively across technical and non-technical audiences, and influence technical decisions beyond your immediate team. - Know how to design, execute, and interpret online experiments and A/B tests to improve user outcomes. - Experience operating distributed machine learning workloads using technologies such as Ray, FSDP, HSDP, or similar frameworks. - Experienced building and maintaining data pipelines and orchestration workflows using technologies such as Flyte, Airflow, BigQuery, and cloud-based storage platforms. ## Location and Benefits - Flexible work location within the North Americas region. - The 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. - Benefits 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. ## Skills ### Required - machine learning - recommendation systems - large language models - PyTorch - Python - Ray - FSDP - HSDP - Flyte - Airflow - BigQuery --- Source: [Apply at jobs.lever.co](https://jobs.lever.co/spotify/9761af44-be41-43ca-8c01-79cdbb2aef93?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)