Ashe Wang
Staff Software Engineer @Suno
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WORK HISTORY
Staff Software Engineer @Suno
Shipped major product features end-to-end: Vox Personas (AI voice cloning), Studio (collaborative music editor with versioning), Stems, and Lyrics Mashups—from data modeling through API to frontend - Built lyrics generation infrastructure: Integrated 10+ LLM providers, designed A/B experimentation framework, implemented content moderation, and iteratively engineered prompts to improve output quality - Developed audio fingerprinting systems for content identification and neural audio embeddings - Optimized platform for scale: Batch query optimizations, Redis caching strategies, DynamoDB migrations, and hybrid storage architectures to reduce latency and handle 1M+ DAU load - Built evaluation frameworks for AI agents: Designed test suites for infinite loop detection, hallucination detection, and response quality scoring with LLM-as-a-judge; integrated Datadog LLM Observability for production monitoring
EDUCATION
Peking University
Bachelor of Science (B.S.), Mathematical Statistics and Probability
UCLA
Statistics
ABOUT ASHE WANG
Experienced in large-scale ads infrastructure (targeting, coarse-to-fine ranking, compliance & regulation) and recommendation systems, with a strong foundation in high-throughput data pipelines built using Kafka, Flink, and Spark.Currently focused on lyrics generation systems, with hands-on experience building and operating LLM workflows for lyrics in production. This includes prompt engineering for creative text, multi-LLM model selection and routing via OpenRouter, and fine-tuning models to improve lyrical coherence, style control, and diversity.Specialized in lyrics evaluation and observability, designing evaluation pipelines to measure quality, stylistic adherence, repetition, and regression across models. Experienced with DeepEval and custom metrics for tracking quality, latency, cost, and model drift in real-world lyrics generation systems.Strong systems background with proficiency in C++, Go, and Python, production experience across AWS and GCP, and hands-on experience with PyTorch and large-scale data processing.
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