Senior Software Engineer, Logistics Platform
New
W
WhatnotLogistics platform
Team members in this role must live within commuting distance of our Los Angeles hub.Full-TimeSenior
Salary$190K - $230K; $190K – $230K • Offers Equity; Compenstation: $190K – $230K • Offers Equity
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Job Details
- Required Skills
- Artificial IntelligenceRESTful APIs
Requirements
- Experience building and owning meaningful backend or platform systems in production at senior scope.
- Ability to develop moderate-to-complex software and disambiguate meaningful problems.
- Ability to collaborate effectively with stakeholders and cross-functional partners.
- Strong product judgment and 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 interacts with messy real-world state.
- Strong instincts around data quality, source-of-truth systems, observability, and operational debugging.
- Thoughtful use of AI to improve engineering work, with verification and independent judgment where needed.
- Comfort working in fast-moving, high-ownership environments with ambiguity and shifting priorities.
- Nice to have: experience in rideshare coordination, food delivery, marketplace fulfillment, trust/risk operations, or order tracking and exception-handling systems.
- Nice to have: experience working on a marketplace or other two-sided platform.
- Nice to have: direct experience in logistics, shipping, or fulfillment systems.
Responsibilities
- Build and improve source-of-truth systems, APIs, and internal platform interfaces for logistics workflows and decisions.
- Own backend and platform work across logistics data quality, shipment state normalization, observability, and operational debugging surfaces.
- Partner with Product, Data Science, CX, Ops, Trust, and Finance to turn logistics problems into durable systems and better user outcomes.
- Improve the correctness, timeliness, and usability of logistics data used by support, trust, product, and operational workflows.
- Build tooling, monitoring, and debugging primitives that help teams detect problems earlier and investigate issues.
- Use AI in day-to-day engineering work while applying independent judgment about correctness and risk.
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