spotify - Machine Learning Engineer - Personalization, Horizon
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Requirements
• An experienced ML practitioner motivated to work on complex real-world problems in a fast-paced and collaborative environment • Strong background in machine learning, natural language processing, and generative AI, with experience in applying theory to develop real-world applications • Hands-on expertise with implementing end-to-end production ML systems at scale. Experience with production LLM scale based systems is a plus • Experience with incorporating human feedback to improve LLM based systems using technicals like DPO, KTO, and reinforcement fine-tuning • Experience with designing end-to-end tech specs and modular architectures for ML frameworks in complex problem spaces in collaboration with product teams • Experience with large scale, distributed data processing frameworks/tools like Apache Beam, Apache Spark, and cloud platforms like GCP or AWS • ## Where You'll Be • We offer you the flexibility to work where you work best! For this role, you can be within the European region as long as we have a work location. • This team operates within the GMT/CET time zone for collaboration. • Excluding France due to on-call restrictions. • 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.
Responsibilities
• Design, build, evaluate, and ship agentic based features and interactive experiences to bring our products to the next level • Collaborate with cross functional teams spanning user research, design, data science, product management, and engineering to build new product features that advance our mission to connect artists and fans in personalized and useful ways • Prototype new approaches and productionize solutions at scale for our hundreds of millions of active users • Promote and role-model best practices of ML systems development, testing, evaluation, etc., both inside the team as well as throughout the organization • Actively contributed to a strong community of machine learning practitioners at Spotify
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