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Tier-1 brand, mid-level ML title, metro location, and broad skillset increase candidate competition.
Core ML engineering and infrastructure skills are broadly transferable across industries.
Explicit years, mandatory ML infra experience, and PyTorch/JAX requirements create strict filters.
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Design, build, and operate scalable machine learning pipelines and online serving systems focused on music catalog metadata quality.
Collaborate with applied scientists and cross-functional teams to optimize ML model performance and deliver production-quality end-to-end solutions.
Monitor, troubleshoot, and support high-volume, low-latency ML systems to detect and correct metadata anomalies in real-time.
3+ years of professional software development experience (non-internship).
2+ years experience in system design or architecture involving design patterns, reliability, and scaling.
Experience with PyTorch or JAX frameworks.
2+ years building large-scale machine learning infrastructure for online recommendation, ads ranking, personalization, or search.
Experienced in developing and operating scalable ML systems with low latency and high volume requirements in production environments.
Strong collaborative ability to work closely with product managers, applied scientists, and engineers to deliver customer-focused solutions.
Solid foundation in ML fundamentals and infrastructure, ideally with knowledge of large language models (LLMs) and related technologies.