Machine Learning Platform Engineer at whatnot

This role is designed for a mid-to-senior level engineer with at least 4 years of experience who sits at the intersection of DevOps, Infrastructure, and Machine Learning. The ideal candidate has moved beyond just training models; you are an expert at building the "plumbing" for AI, including low-latency serving, GPU orchestration, and distributed training pipelines for LLMs. The compensation is highly competitive, topping out at $320,000 base plus equity. While the culture emphasizes high autonomy and "building from scratch," the company offers robust corporate benefits, including a 4% 401k match, 16 weeks of parental leave, and a unique "dogfooding" budget to buy items on the platform. You’ll have the flexibility to work from home, provided you can commute to a hub in SF, NY, Seattle, or LA. **You might be a good fit if you...** * Have professional experience scaling LLM inference and distributed training using GPUs. * Are proficient in Python and comfortable with cloud-native tools like AWS Sagemaker, EKS, and Kafka. * Enjoy the "builder" phase of a company—moving fast to productionalize models for search, recommendations, and fraud detection. * Live within commuting distance of one of the four specified US coastal hubs.