Software Engineer; Logistics Platform

🚀 Join the Future of Commerce with Whatnot!

Whatnot is the largest livestream shopping platform in North America and Europe to buy, sell, and discover the things you love. Whether it's trading cards, fashion, electronics, or live plants, our sellers are building real businesses across hundreds of categories. We're building live commerce at a scale that's never been done in the West, and there's no playbook to copy. The people here are shaping how an entirely new industry develops.

As a remote co-located team, we're inspired by our values and anchored in hubs across the US, UK, Ireland, Poland, Germany, and Australia. We move fast, stay close to our users, and focus on the work that drives the most impact.

We're one of the fastest growing marketplaces and were recently named the #1 Best Startup Employer in America by Forbes. Check out the latest Whatnot updates on our news and engineering blogs and join us as we enable anyone to turn their passion into a business and bring people together through commerce.

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.
As a Software Engineer on Logistics Platform, you will:

• 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.

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 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.

We’re especially excited about candidates who have:

• 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…