Machine Learning Platform Engineer at whatnot

This role is ideal for a mid-to-senior level engineer (4+ years experience) who bridges the gap between Core Infrastructure and Machine Learning. You should have a strong background in productionizing ML models at scale, with specific expertise in Python and distributed systems. The team is looking for a "builder" who can handle the complexities of low-latency GPU inference and high-throughput data pipelines within a fast-paced marketplace environment. The compensation is highly competitive, starting at $245k base plus equity and a comprehensive benefits package (including a 4% 401k match and family planning benefits). While the company is "remote co-located," this specific role requires being within commuting distance of their Bay Area, Seattle, NYC, or LA hubs for in-person collaboration. **You might be a good fit if you...** * Have experience scaling LLMs and managing GPU-accelerated infrastructure. * Are proficient with the AWS ecosystem (Sagemaker, EKS) and data tools like Kafka or Flink. * Enjoy "dogfooding" the product you build and thrive in a high-growth startup culture. * Can work autonomously to bridge the gap between ML research and reliable production deployment.