Software Engineer, Whatnot

• Team members in this role must live within commuting distance of our Los Angeles hub
• People who do well at Whatnot tend to be comfortable figuring things out as they go, biased toward action, and genuinely curious about what they're building
• They care more about outcomes than credit and stay close to the product and the people using it
• Experience building and owning meaningful backend or platform systems in production at the senior scope — i.e. developing and owning moderate-to-complex software, disambiguating meaningful problems, and collaborating effectively with stakeholders and cross-functional partners
• Strong product judgment and the ability to connect technical work to user impact, business outcomes, and explicit trade-offs
• Experience in complex marketplace, coordination, delivery, fulfillment, trust, or post-purchase systems where software decisions interact with messy real-world state
• Strong instincts around data quality, source-of-truth systems, observability, and operational debugging
• A thoughtful approach to AI: not just using AI for more output, but using it to improve the quality of engineering work while knowing where verification and independent judgment still matter
• Comfort operating in fast-moving, high-ownership environments with ambiguity and shifting priorities

Desirable

• Shipping is one of the most operationally complex and business-critical parts of Whatnot. It directly shapes buyer trust, seller confidence, CX burden, trust and enforcement decisions, and shipping economics
• The Logistics Platform team builds the shared systems, data, and capabilities beneath shipping on Whatnot. That includes core logistics data, tracking and scan normalization, platform interfaces, and the observability and investigation surfaces that other teams rely on
• We’re looking for a Senior Software Engineer, Logistics Platform to help make those systems more trustworthy, operable, and easy to build on. This is a product-minded platform role: much of the work is internal-facing, but the impact is very external — better buyer and seller experience, fewer support issues, stronger trust decisions, and better margin outcomes
• Build and improve source-of-truth systems, APIs, and internal platform interfaces that other teams depend on for logistics workflows and decisions
• Own backend and platform work across logistics data quality, shipment state normalization, observability, and operational debugging surfaces
• Partner closely with Product, Data Science, CX, Ops, Trust, and Finance to turn messy real-world logistics problems into durable systems and better user outcomes
• Improve the correctness, timeliness, and usability of the logistics data that powers support, trust, product, and operational workflows
• Build tooling, monitoring, and debugging primitives that help teams detect problems earlier and get to truth faster
• Use AI thoughtfully in day-to-day engineering work to improve quality, learning speed, debugging, and execution — while keeping a strong independent bar for correctness and risk judgment