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# Staff Software Engineer, Fleet Management

- **Company:** Tesla Motors
- **Location:** Austin, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Algorithm Design, Automated Storage and Retrieval Systems, Distributed Systems, Firmware, Job Scheduling, Message Broker, MySQL, Routing, Software Engineering, SQL Databases, Large Language Models, Multi-Agent Systems, Event Driven Architecture, Kubernetes, Low Latency, Production Code, Apache Kafka, Graphql, Grpc, Scheduling Algorithms, Docker
- **Published:** October 15, 2026
- **Apply:** https://jobs.localjobnetwork.com/apply/add/86880093/1

## About the Role

* 7+ years of software engineering experience, with 3+ years focused on multi-agent systems, fleet management,logisticsoptimization, or robotics coordination

 * Deepexpertisein at least two of: job scheduling algorithms, vehicle routing, traffic management, conflict resolution, resource allocation

 * Demonstrated experience building andoperatingdistributed systems in production-event-driven architectures, message brokers, service decomposition, and the operational realities of running them

 * Strongproficiencyin Go; working familiarity with the broader stack:GraphQL,gRPC, NATS or Kafka, SQL (MySQL), Temporal, Docker, Kubernetes, and Helm

 * Experience shipping algorithmic systems into real production environments, not just research or simulation

 * Track recordof technical leadership: setting architectural direction, mentoring engineers, and driving technical decisions across teams

 * Strong product sense with the ability to understand user pain, domain constraints, and independently drive toward a better product

 * Experience with real-time or near-real-time decision systems-you understand latency, throughput, and reliability constraints

 * An AI-forward mindset with a drive to incorporate AI-driven approaches into product development, whether that is ML for scheduling optimization, LLMs for operational tooling, or AI-assisted development workflows

 * Background in warehouse automation (WMS/WES/WCS), AMR/AGV fleet management, or factory material flow is a plus

## Description

We are seeking a Staff Software Engineer to lead the Fleet Management domain. You will own the technical direction, write production code, and lead a team of engineers while scaling the system to support more factories, more robots, and more complex material flows. The ideal candidate combines deepexpertisein multi-agent scheduling or fleet coordination with hands-on distributed systems experience, strong product instincts, and an AI-forward mindset.

This position is based in Austin, TX.
What You'll Do

 * Own the technical direction and algorithmic design for job scheduling, routing, traffic and resource management, and fleet orchestration

 * Lead and grow a team of engineers: mentor on algorithm design and systems thinking, review technical approaches, and set the bar for quality and rigor

 * Write production code in Go - this is a hands-on technical leadership role, not a management position

 * Design and evolve scheduling algorithms that handle multi-robot coordination, deadlock avoidance, dynamic replanning, and priority-based dispatching

 * Drive architectural decisions across the fleet management stack: Go,GraphQL,gRPC, NATS, Kafka, Temporal, MySQL, and Kubernetes

 * Identifyopportunities to apply AI and machine learning to scheduling, routing, anomaly detection, and operational decision-making

 * Own the interface between fleet-level decisions and vehicle-level execution, working closely with the Firmware Controls team to ensure correctness under real-world conditions

 * Partner with the Integration team tovalidatethat algorithms perform reliably in live factory environments and feed production learnings back into system design

 * Translate operational pain points and user needs into technical direction, driving the product toward measurable improvements in factory throughput and uptime

 * Drive the technical roadmap for fleet management: scaling to new factories, reducing downtime, and improving scheduling efficiency