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Suno is building the world's first creative entertainment platform for music creation, backed by leading investors including Bond Capital, Menlo Ventures, Lightspeed Venture Partners, and NVIDIA Ventures. The company is the fastest-growing consumer entertainment company and leader in AI music.
You'll join the early members of Suno's machine learning recommendations team, working closely with the founding team on state-of-the-art recommendation models for music discovery. This is a high-ownership role where you'll make critical technical decisions on how the platform discovers and surfaces music to millions of users.
Key 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 operating 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
The role offers significant impact: you'll shape how millions of users discover music and express themselves through a new medium. Suno emphasizes ownership, intensity, and quality work in a fast-paced environment.
Benefits include company equity, 401(k) with 3% employer match, comprehensive medical/dental/vision insurance, 11 paid holidays plus unlimited PTO, 16 weeks paid parental leave, creative education stipend, commuter allowance, and in-office lunch daily.
Requirements:
- Strong background in applied mathematics, statistics, machine learning, or related quantitative field (PhD or equivalent experience required)
- 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 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 desire to understand systems deeply rather than treating models as black boxes
- Strong plus: love of music (listening, exploring, or making)
- Must be eligible to work in the US
