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Jobs (6)

22 AI · 206 total active
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Software Engineer, Machine Learning Infrastructure
Software Engineer, Machine Learning Infrastructure at Whatnot, focusing on scaling AI and ML infrastructure for large language models and other ML applications. Responsibilities include owning AI/ML infrastructure, prototyping and productionizing ML architectures, designing and scaling inference infrastructure for low-latency and high-throughput serving, and building distributed training and inference pipelines.
ServePost-trainEngineeringSan Francisco, CA3w ago8
Software Engineer, Machine Learning Infrastructure
Software Engineer, Machine Learning Infrastructure at Whatnot, focusing on building and scaling the core infrastructure for AI and ML models, including low-latency large model serving and distributed training/inference pipelines.
Serve
Engineering
San Francisco, CA
3w ago
8
Machine Learning Platform Engineer
Machine Learning Platform Engineer at Whatnot, focusing on building and scaling the core infrastructure for AI and ML models, including LLM applications, low-latency serving, distributed training, and GPU inference.
ServePost-trainEngineeringSan Francisco, CAMar 38
Technical Lead Manager, ML Infrastructure
Lead the development and scaling of core ML infrastructure, including low-latency model serving, streaming feature ingestion, distributed training, and high-throughput GPU inference, to power AI/ML applications at consumer scale. This role involves hands-on coding, architectural guidance, and empowering ML scientists.
ServeDataEngineeringSan Francisco, CAFeb 278
Machine Learning Infrastructure Engineer
Seeking an ML Infrastructure Engineer to design and scale core infrastructure for ML and LLM applications, focusing on low-latency serving, distributed training, and high-throughput GPU inference to productionize cutting-edge models.
ServePost-trainEngineeringSan Francisco, CAFeb 58
Senior Engineering Manager, ML Platform
Senior Engineering Manager, ML Platform at Whatnot, a livestream shopping platform. This role focuses on leading the development and scaling of core infrastructure for machine learning and self-hosted LLM applications. Responsibilities include building low-latency model serving, streaming feature ingestion, distributed training, and high-throughput GPU inference systems. The role requires strong technical depth, hands-on coding, and managing production ML systems at consumer scale.
ServeDataEngineeringSan Francisco, CAJan 158