Senior/Staff Machine Learning Engineer - Content Discovery

About Suno

We're building the world's first creative entertainment platform, where the entire world can feel the joy and fulfillment of making music. Music is for everyone: Our users include everyone from grandmothers creating songs for their loved ones, to Grammy winners using Suno Studio, our power tool, to make the most popular hits in the world.

Building the future of entertainment requires ambition. The pace is fast, the problems are hard, and the work demands ownership and intensity. For the right people, it’s incredibly rewarding: a chance to shape a new medium, work with a small team that cares deeply about quality, make music, drink too much coffee, and build something that millions of people use to express themselves in ways that were never before possible.

Suno is the fastest growing consumer entertainment company and the leader in AI music. We are backed by leading investors including Bond Capital, Menlo Ventures, Lightspeed Venture Partners, IVP, Forerunner, Union Square Ventures, Alkeon, Quiet, Matrix Partners, Schroders Capital and, NVentures (venture arm of NVIDIA).

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.

Perks & Benefits for Full-Time Employees

• Company Equity Package
• 401(k) with 3% Employer Match & Roth 401(k)
• Medical, Dental, & Vision Insurance (PPO w/ HSA & FSA options)
• 11 Paid Holidays + Unlimited PTO & Sick Time
• 16 Weeks of Paid Parental Leave
• Creative Education Stipend
• Generous Commuter Allowance
• In-Office Lunch (5 days per week)

Suno is proud to be an Equal Opportunity Employer. We consider qualified applicants without regard to race, color, ancestry, religion, sex, national origin, sexual orientation, gender identity, age, marital or family status, disability, genetic information, veteran status, or any other legally protected basis under provincial, federal, state, and local laws, regulations, or ordinances. We will also consider qualified applicants with criminal histories in a manner consistent with the requirements of state and local laws, including the Massachusetts Fair Chance in Employment Act, NYC Fair Chance Act, LA City Fair Chance Ordinance, and San Francisco Fair Chance Ordinance.