Machine Learning Engineer – Content Discovery (on-site) in San Francisco, United States at Suno. What you’ll do: 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; translate abstract objectives (relevance, novelty, diversity, long-term satisfaction) into measurable metrics and optimized systems; run large-scale experiments and causal analyses to evaluate model behavior and product impact; collaborate with product and research leadership to define the technical direction of Suno’s personalization systems. What you’ll need: Strong background in applied mathematics, statistics, machine learning, or a related quantitative field (PhD or equivalent experience); experience designing models from first principles (probabilistic models, optimization-based systems, representation learning, graph-based methods); proficiency in Python and modern ML frameworks (e.g., PyTorch) with the ability to implement and iterate on research ideas; familiarity with learning from user interaction data (implicit feedback, ranking losses, bandits, or reinforcement-learning-adjacent methods); comfort reasoning about tradeoffs between model quality, scalability, and system constraints; curiosity, rigor, and a desire to understand systems deeply rather than treating models as black boxes; a love of music is a strong plus. Addition

Machine Learning Engineer – Content Discovery (on-site) in San Francisco, United States at Suno. What you’ll do: 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; translate abstract objectives (relevance, novelty, diversity, long-term satisfaction) into measurable metrics and optimized systems; run large-scale experiments and causal analyses to evaluate model behavior and product impact; collaborate with product and research leadership to define the technical direction of Suno’s personalization systems. What you’ll need: Strong background in applied mathematics, statistics, machine learning, or a related quantitative field (PhD or equivalent experience); experience designing models from first principles (probabilistic models, optimization-based systems, representation learning, graph-based methods); proficiency in Python and modern ML frameworks (e.g., PyTorch) with the ability to implement and iterate on research ideas; familiarity with learning from user interaction data (implicit feedback, ranking losses, bandits, or reinforcement-learning-adjacent methods); comfort reasoning about tradeoffs between model quality, scalability, and system constraints; curiosity, rigor, and a desire to understand systems deeply rather than treating models as black boxes; a love of music is a strong plus. Addition