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Senior/Staff Machine Learning Engineer (Content Discovery)

Suno

Skills

Required

  • Machine Learning
  • Data Science
  • Python
  • modern ML
  • PyTorch
  • Infra

Preferred

  • Search
  • Ads
  • Optimization
  • Reinforcement
  • Data Mining
  • Information Retrieval
  • RecSys
  • Spark

Join Suno, a cutting-edge music discovery platform, as a Senior/Staff Machine Learning Engineer. In this role, you will have the opportunity to shape the technical direction of our personalization systems, develop mathematical models of user preference, and build scalable recommendation models. You will work closely with the founding team and have ownership of a wide variety of technical decisions. Enjoy a range of benefits, including a retirement match, medical, dental, and vision insurance, a generous commuter benefit, free lunch, unlimited paid time off, and more.

USD 241,200 - 382,935

Responsibilities

  • Formulate and develop mathematical models of user preference, similarity, and engagement for music discovery.
  • Design learning systems that infer user taste from sparse, noisy, and evolving interaction data.
  • Build and deploy scalable recommendation and ranking models that operate under real-time latency and throughput constraints.
  • Solid knowledge and understanding of data pipelines and cloud technologies, e.g., Spark, AWS, etc

Requirements

  • Proficiency in Python and modern ML frameworks (e.g., PyTorch) with the ability to implement and iterate on research ideas
  • Strong communication and collaboration skills working with cross-functional teams such as Product, Data, Infra, etc
  • Master’s or PhD degree in Computer Science, Machine Learning, Data Science, or a related quantitative field, or equivalent practical experience
  • Proven track record of building, deploying, and maintaining 0->1 production Machine Learning or Data Science solutions
  • A love of music (listening, exploring, or making) is a strong plus

Preferred

  • 3+ years of industrial experience in Recommendation, Search, Ads, Optimization, Reinforcement Learning, or related disciplines
  • Publications at top Machine Learning, Data Mining, Information Retrieval, Natural Language Processing conferences such as KDD, ICML, NeurIPS, RecSys, ACL, etc