About the Role
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.
Check out the Suno version of the job here! https://suno.com/s/ujpqmS3Rf9Qg911A
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
Work closely with product and research leadership to define the technical direction of Suno’s personalization systems
What You’ll Need
Master’s or PhD degree in Computer Science, Machine Learning, Data Science, or a related quantitative field, or equivalent practical experience
Proficiency in Python and modern ML frameworks (e.g., PyTorch) with the ability to implement and iterate on research ideas
Solid knowledge and understanding of data pipelines and cloud technologies, e.g., Spark, AWS, etc.
Proven track record of building, deploying, and maintaining 0->1 production Machine Learning or Data Science solutions
Strong communication and collaboration skills working with cross-functional teams such as Product, Data, Infra, etc.
A love of music (listening, exploring, or making) is a strong plus
Preferred Qualifications
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.
Additional Notes: Applicants must be eligible to work in the US.