Software Engineer, Twitch

• 1+ years of professional software development experience with a focus on building scalable systems
• Experience building production ML infrastructure, including model deployment, serving, and monitoring systems
• Proficiency in modern programming languages (Python, Java, Go) and distributed system technologies
• A track record of building APIs and integrating systems that handle high-throughput, low-latency requirements
• Understanding of data processing pipelines and experience with streaming technologies (Flink, Kafka, or similar)
• Sharp problem-solving skills with a focus on algorithms, data structures, and distributed system design
• Bachelor's degree in Computer Science, Engineering, or equivalent real-world experience

Desirable

• Join the Monetization ML team within Twitch's Commerce organization, where we're building the intelligent systems that power personalized experiences for creators and viewers across all monetization products
• We are the team behind the ML infrastructure for ads, commerce, and money products – systems that optimize revenue and detect fraud while serving millions of users in real-time
• From production model infrastructure to API integrations across services, we're constantly pushing the boundaries of what's possible in live streaming monetization
• Our team are based in Seattle, WA and New York City, NY
• Build robust ML infrastructure and platforms that power personalized monetization experiences across Twitch's ecosystem
• Architect and develop APIs that seamlessly integrate ML models with existing monetization services, enabling real-time and batch predictions at scale
• Collaborate with Applied Scientists to productionize ML models, transforming research into production-ready systems that serve millions of users
• Partner with product engineering teams across monetization to deliver ML-powered features that enhance creator and viewer experiences
• Design and implement comprehensive monitoring and operational excellence systems for ML model performance, ensuring reliability and quality at scale
• Develop data ingestion and processing pipelines that support both real-time inference and batch training workflows
• Explore and build infrastructure for emerging use cases including LLM applications in monetization contexts