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# Machine Learning Recommendations Engineer Song

- **Company:** Suno Inc.
- **Location:** San Francisco, CA, United States
- **Contract:** Permanent contract
- **Skills:** Human-Computer Interaction, Python (Programming Language), Machine Learning, Tensorflow, Pytorch, Build Management, Feature Extraction
- **Published:** October 2, 2026
- **Apply:** https://startup.jobs/machine-learning-engineer-content-discovery-suno-10247224

## About the Role

* Strong background in applied mathematics, statistics, machine learning, or a related quantitative field (PhD or equivalent experience)
 * Experience designing models from first principles (e.g., 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 (listening, exploring, or making) is a strong plus

Additional Notes: Applicants must be eligible to work in the US.

## Description

We're looking for early members of our machine learning recommendations team. You'll work closely with the founding team and have ownership of a wide variety of technical decisions on how we build and deploy our state of the art recommendation models., * 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
 * Work closely with product and research leadership to define the technical direction of Suno's personalization systems


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