Feature Platform Engineer

Whatnot · Consumer · San Francisco, CA · Engineering

This role focuses on building and scaling the feature ingestion and storage infrastructure that powers both core business logic and ML applications. The engineer will work on real-time feature pipelines, optimize system performance, and empower ML scientists to iterate faster by building abstractions and tools. The goal is to enable faster ML model responses to marketplace dynamics and scale AI across the ecosystem.

What you'd actually do

  1. Own the feature ingestion and storage infrastructure powering both core business logic and ML models across critical business surfaces–supporting growth, recommendations, trust and safety, fraud, seller tooling, and more.
  2. Push the boundaries of real-time decisioning, ensuring Whatnot’s ML models respond ever faster to constantly changing marketplace dynamics.
  3. Design and evolve real-time feature pipelines that feed both our online and offline stores, ensuring sub-second latency, high reliability, and model training fidelity.
  4. Optimize system performance by managing resource utilization and developing intelligent feature caching strategies.
  5. Empower scientists to iterate faster by building abstractions, APIs, and developer tools that simplify the development of near-realtime features.

Skills

Required

  • 4+ years of professional experience developing machine learning systems and algorithms
  • 3+ years of software engineering experience building and maintaining production systems for consumer-scale loads
  • 1+ years of professional experience developing software in Python
  • Ability to work autonomously and drive initiatives across multiple product areas and communicate findings with leadership and product teams.
  • Experience with operational, search, and key-value databases such as PostgreSQL, DynamoDB, Elasticsearch, Redis.
  • Firm grasp of visualization tools for monitoring and logging e.g. DataDog, Grafana.
  • Familiarity with cloud computing platforms and managed services such as AWS Sagemaker, Lambda, Kinesis, S3, EC2, EKS/ECS, Apache Kafka, Flink.
  • Professionalism around collaborating in a remote working environment and well tested, reproducible work.
  • Exceptional documentation and communication skills.

Nice to have

  • Bachelor’s degree in Computer Science, Statistics, Applied Mathematics or a related technical field, or equivalent work experience.

What the JD emphasized

  • scale AI across Whatnot’s ecosystem

Other signals

  • powers critical business logic & machine learning applications
  • near-real-time signals
  • real-time feature pipelines
  • sub-second latency
  • optimize system performance
  • intelligent feature caching strategies
  • simplify the development of near-realtime features
  • scale AI across Whatnot’s ecosystem