Machine Learning Platform Engineer

Whatnot · Consumer · San Francisco, CA · Engineering

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.

What you'd actually do

  1. Own the infrastructure powering AI and ML models across critical business surfaces–supporting growth, recommendations, trust and safety, fraud, seller tooling, and more.
  2. Prototype, deploy, and productionalize novel ML architectures that directly shape user experience and marketplace dynamics.
  3. Design and scale inference infrastructure capable of serving large models with low latency and high throughput.
  4. Build distributed training and inference pipelines leveraging GPUs and both model and data parallelism.
  5. Stretch beyond your comfort zone to take on new technical challenges as we scale AI across Whatnot’s ecosystem.

Skills

Required

  • Python
  • software engineering
  • production systems
  • PostgreSQL
  • DynamoDB
  • Elasticsearch
  • Redis
  • DataDog
  • Grafana
  • AWS Sagemaker
  • Lambda
  • Kinesis
  • S3
  • EC2
  • EKS/ECS
  • Apache Kafka
  • Flink

Nice to have

  • machine learning systems
  • algorithms
  • cloud computing platforms
  • managed services

What the JD emphasized

  • low-latency
  • large models
  • distributed training
  • high-throughput GPU inference

Other signals

  • ML infrastructure
  • LLM applications
  • low-latency serving
  • distributed training
  • GPU inference