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 at Whatnot. The engineer will work with ML scientists to leverage near-real-time signals for critical business surfaces like growth, recommendations, trust and safety, and fraud. Key responsibilities include designing and evolving real-time feature pipelines for online and offline stores, optimizing system performance, and empowering scientists with abstractions and tools for near-real-time features. The role requires experience with production ML systems, software engineering for consumer-scale loads, Python, and various databases and cloud platforms.

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
  • 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.

Nice to have

  • Bachelor’s degree in Computer Science, Statistics, Applied Mathematics or a related technical field, or equivalent work experience.
  • Ability to work autonomously and drive initiatives across multiple product areas and communicate findings with leadership and product teams.
  • Professionalism around collaborating in a remote working environment and well tested, reproducible work.
  • Exceptional documentation and communication skills.

What the JD emphasized

  • 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.

Other signals

  • feature ingestion and storage infrastructure
  • ML applications
  • near-real-time signals
  • real-time decisioning
  • real-time feature pipelines