Senior Software Engineer, Autonomous Freight Systems

Flexport•San Francisco, CA

21h•$183,000 - $229,000•Onsite

About The Position

The Autonomous Freight Systems team is a brand new, AI-first engineering team in San Francisco, building Flexport's client-facing rates platform and self-serve freight booking experience from the ground up. We own two of the highest-leverage surfaces in the Client App: how clients see pricing and how they book freight without manual intervention. As a Senior Engineer, you will own significant technical domains end-to-end, not just features, but whole problem spaces. You will drive the engineering of systems that power rate visibility, pricing intelligence, and AI-assisted booking across ocean, air, and trucking. You don't wait to be told what to build next; you identify the highest-leverage problems, propose solutions, and see them through to production. You will be a technical force multiplier on the team: raising the bar on code quality, helping junior engineers grow, and shipping with a velocity and confidence that comes from deep ownership. This is a ground-floor opportunity to build the platform that moves Flexport from an assisted-sales model to a tech-run one for the long tail of our client base—turning the complexity of global trade into a seamless digital experience for thousands of shippers.

Requirements

5+ years of professional software engineering experience with a proven track record of shipping production-grade systems with increasing levels of independence.
Deep Ownership Instinct: You hold yourself accountable for outcomes, not just outputs. When something breaks, you fix it—and you make sure it doesn't break again.
Full-Stack Fluency: You can own a feature from the database to the browser. You are opinionated about schema design, API contracts, and frontend architecture, not just one layer.
AI/LLM Hands-On Experience: You have built software that integrates LLMs into real user workflows—not just proof-of-concepts, but production systems. You understand the practical challenges: latency, reliability, prompt fragility, and cost.
Agency and Drive: You identify the next most important problem without being asked. You default to action, propose solutions, and bring others along.
Technical Rigor: A genuine passion for simple, well-tested, well-structured code. You push back on complexity and leave systems better than you found them.
Communication: You can explain a technical tradeoff clearly to a product manager and to a staff engineer. Your design docs and code reviews are worth reading.
Proficiency with deploying, monitoring, and maintaining production services on cloud infrastructure (AWS).

Nice To Haves

Experience with elements of our tech stack: Java, Spring Boot, Ruby on Rails, React, AWS.
Familiarity with agentic patterns, RAG, or workflow orchestration (e.g., Temporal).
Experience in logistics, supply chain, freight technology, or pricing systems.
A bias for action: when you see a problem, you own the fix.

Responsibilities

Take end-to-end technical ownership of core systems: from schema design and backend APIs to the React surfaces our customers use and drive them to production without needing oversight.
Proactively identify gaps, inefficiencies, and engineering problems within your domain and lead the effort to resolve them.
Design and build AI-powered features and integrations, moving beyond "AI familiarity" to hands-on implementation of agent workflows and LLM-backed automation in production.
Drive technical design for significant features, producing well-reasoned proposals and aligning teammates before writing the first line of code.
Mentor and support P2 engineers through code review, pairing, and design feedback, raising the quality of the team's output.
Collaborate closely with product, design, pricing, and operations to translate complex domain problems into elegant, production-ready solutions.
Build and ship incrementally: this team deploys to production dozens of times per day, and you are comfortable with that pace.
Ensure reliability and quality by building in metrics, monitoring, and applying learnings from incidents to all systems you own.