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Design, build, and scale backend systems for a social AI platform. Collaborate with product, design, and data teams to deliver performant microservices, optimize user acquisition and retention, and improve platform scalability and reliability.

The ML Engineer will build and scale large-scale data systems and pipelines, focusing on workload orchestration, dataset curation, and quality improvement techniques for machine learning models.

Design, deploy, and scale low-latency inference infrastructure for generative audio models (TTS, ASR, voice conversion). Build high-performance inference engines, Kubernetes-based autoscaling, CI/CD pipelines, observability, and GPU optimization to bridge research and production for streaming and batch workloads.