Senior/Staff Machine Learning Engineer - Content Discovery

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. Pro members see the company and apply now. It opens to everyone on November 1.

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

What they're looking for

• 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

Nice to have

• 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.