Feature Platform Engineer
WhatnotSan Francisco, CA$245k – $345kPosted 9 March 2026
Job Description
Feature Platform Engineer
🚀 JOIN THE FUTURE OF COMMERCE WITH WHATNOT!
Whatnot is the largest live shopping platform in North America and Europe to buy, sell, and discover the things you love. We’re re-defining e-commerce by blending community, shopping, and entertainment into a community just for you. As a remote co-located team, we’re inspired by innovation and anchored in our values https://www.whatnot.com/careers. With hubs in the US, UK, Germany, Ireland, Poland, and Australia, we’re building the future of online marketplaces –together.
From fashion, beauty, and electronics to collectibles like trading cards, comic books, and even live plants, our live auctions have something for everyone.
And we’re just getting started! As one of the fastest growing marketplaces https://a16z.com/marketplace-100/, we’re looking for bold, forward-thinking problem solvers across all functional areas. Check out the latest Whatnot updates on our news https://blog.teamwhatnot.com/ and engineering https://medium.com/whatnot-engineering blogs and join us as we enable anyone to turn their passion into a business, and bring people together through commerce.
💻 ROLE
We’re looking for builders–intellectually curious, highly entrepreneurial engineers eager to shape the future of AI and ML at Whatnot. You’ll design and scale the feature ingestion and storage infrastructure that powers critical business logic & machine learning applications across the company. You’ll work side by side with machine learning scientists to make the most of near-real-time signals and unlock entirely new product experiences.
WHAT YOU'LL DO:
- 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.
- Push the boundaries of real-time decisioning, ensuring Whatnot’s ML models respond ever faster to constantly changing marketplace dynamics.
- 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.
- Optimize system performance by managing resource utilization and developing intelligent feature caching strategies.
- Empower scientists to iterate faster by building abstractions, APIs, and developer tools that simplify the development of near-realtime features.
- Stretch beyond your comfort zone to take on new technical challenges as we scale AI across Whatnot’s ecosystem.
US Based: We offer flexibility to work from home or from one of our global office hubs, and we value in-person time for planning, problem-solving, and connection. Team members in this role must live within commuting distance of our New York, Seattle, Los Angeles, and San Francisco hubs.
👋 YOU
Curious about who thrives at Whatnot? We’ve found that low ego, a growth mindset, and leaning into action and high impact goes a long way here.
As our next AI/ML Engineer you should have 4+ years of professional experience developing machine learning systems and algorithms, plus:
- Bachelor’s degree in Computer Science, Statistics, Applied Mathematics or a related technical field, or equivalent work experience.
- 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 work ... (truncated, view full listing at source)
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