Machine Learning Engineer - Content Discovery

1 нед. назад

241.2k–382.9k USD / годСШАOnsite

machine learningrecommendation systems

Develop and deploy state-of-the-art recommendation models as an early member of the machine learning recommendations team at .

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 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.
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).

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.
https://suno.com/s/ujpqmS3Rf9Qg911A
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 ’s personalization systems

Strong background in applied mathematics, statistics, machine learning, or a related quantitative field (PhD or equivalent experience)
Experience designing models from first principles (e.g., probabilistic models, optimization-based systems, representation learning, graph-based methods)
Proficiency in Python and modern ML frameworks (e.g., PyTorch) with the 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 a desire to understand systems deeply rather than treating models as black boxes
A love of music (listening, exploring, or making) is a strong plus
Additional Notes: Applicants must be eligible to work in the US.