# Staff Machine Learning Engineer, Home Surfaces **Company**: Spotify **Location**: New York, NY **Work arrangement**: remote **Experience**: staff **Job type**: Permanent **Salary**: $227,495- $324,993 **Category**: Engineering **Industry**: Technology **Apply**: https://jobs.lever.co/spotify/6af8894a-752f-4485-be70-a6685e5fd696?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_3ea59489-6be ## Description The Personalization team at Spotify makes deciding what to play next easier and more enjoyable for every listener. As a Staff Machine Learning Engineer on the Surfaces Podcasts team, you'll 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 for listeners at global scale. ## 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. ## Requirements - Deep experience building and deploying machine learning systems in production environments. - Deep experience with recommendation systems, ranking models, personalization, or large-scale content discovery platforms. - Proficient in Python and have 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 about creating high-quality user experiences through thoughtful applications of machine learning. - Communicate effectively with technical and non-technical partners and can 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. - Experience 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 - Python - PyTorch - machine learning - recommendation systems - large language models - SFT - distillation - LoRA - Ray - FSDP - HSDP - Flyte - Airflow - BigQuery --- Source: [Apply at jobs.lever.co](https://jobs.lever.co/spotify/6af8894a-752f-4485-be70-a6685e5fd696?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)