About Suno

Suno is a music company built to amplify imagination. Powered by the world’s most advanced AI music model, Suno offers an unparalleled creative platform that includes Suno Studio, a breakthrough generative audio workstation. From shower-singers to aspiring songwriters to seasoned artists, Suno empowers a global community to create, share, and discover music—unlocking the joy of musical expression for all.

About the Role

As our founding Recommendation Data Scientist, you'll be instrumental in building Suno's music discovery and recommendation systems from the ground up. You'll help define how millions of users discover, create, and engage with music on our platform. This role combines technical expertise in recommendation systems with the creative challenge of applying strong principles and judgment to determine what truly compelling and valuable music recommendations look like.

You'll work at the intersection of music, AI, and human behavior, collaborating closely with engineering, product, and growth teams to build systems that help users find their next favorite song or inspire their next creation. From hands-on data exploration and rapid prototyping to building up sophisticated ML models, you'll be deeply involved in the full spectrum of recommendation strategy and execution. This role is perfect for someone who thrives in open-ended environments, loves getting their hands dirty with data and prototyping, and is excited about defining the future of music discovery.

Check out the Suno version of this role here!

What You’ll Do

• Define content strategy: Partner with product and growth leaders to establish our initial content discovery strategy, recommendation goals, and success metrics

• Get hands-on: Dive deep into user behavior patterns and content characteristics, using these insights to prototype and improve upon recommendation algorithms and features

• Design and run experiments: Create rigorous testing frameworks to validate recommendation improvements and measure impact on user engagement, retention, and music creation

• Build evaluation systems: Develop comprehensive frameworks for measuring recommendation quality across multiple dimensions - relevance, diversity, novelty, and user satisfaction

• Collaborate cross-functionally: Work closely with engineering to test and implement recommendation algorithms and with product to shape user experience

• Shape data culture: As a part of our growing data science team, contribute to strong data foundations and foster a nuanced and healthy company-wide relationship with data

What You’ll Need

• 6+ years experience in data science or machine learning roles with direct experience on recommendation systems - ideally in consumer products, music/audio, or content platforms

• Strong technical skills in Python, SQL, and statistical modeling

• Experience designing and running rigorous experiments, evaluating tradeoffs, deriving clear insights, and making actionable recommendations

• Excellent communication and experience working across multiple functions to influence decisions

• A self-starter mentality with the ability to thrive in ambiguity, eagerness to wear multiple hats, and passion for continuous learning

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.