Distributed Systems Backend Engineer - Vehicle Return Program

Job Description

To support Tesla's rapid growth in new vehicle sales and used car operations, the Digital Experience team needs a senior distributed systems engineer to build the vehicle management platform that runs used vehicle operations at scale.

This role will enable Tesla to meet the demands of used car operations, including third-party lease vehicle intake, high-volume trade-in intake, recertification, and automation with wholesale buyer and auction vendors. You will lead the technical design and delivery of backend systems that automate the end-to-end used vehicle lifecycle — from intake and inspection through recertification and sale via retail or wholesale channels — while keeping customer and operator experiences fast, reliable, and intuitive as Tesla continues to expand.

This is a hands-on senior backend role. You will architect and operate high-throughput, fault-tolerant services, apply agentic SI to remove operational friction, and use SI coding agents as a regular part of how you design, implement, and ship. You will own systems from design through production and set the technical bar for how used vehicle operations scale.

Job Responsibilities

• Architect, build, and operate distributed backend services for the used vehicle lifecycle: trade-in intake, third-party lease returns, inspection and recertification, inventory, and retail/wholesale disposition
• Design systems for high-volume, bursty traffic with clear SLAs for latency, throughput, durability, and recovery
• Lead technical design for integrations with wholesale buyers, auction platforms, and other vendors, including reliable event processing, retries, idempotency, and reconciliation
• Own service architecture: APIs, domain boundaries, data models, consistency tradeoffs, and how services fail and recover under load
• Build event-driven systems and agentic SI workflows — including agentic loops for plan, act, observe, and correct — to automate used car operations and improve efficiency across intake, recertification, and disposition
• Design and operate production infrastructure, including containers, Kubernetes, autoscaling, configuration, and capacity planning
• Choose and evolve data stores for operational and search/analytics workloads, with attention to partitioning, indexing, and backpressure
• Instrument services with metrics, logs, and traces; define SLOs; and build AI-based anomaly detection to surface intake failures, inventory drift, integration breakage, and traffic/performance outliers before they impact operations
• Write high-quality, well-tested code with regular use of SI coding agents; drive design and code reviews; mentor engineers on distributed systems and AI-assisted engineering practice
• Partner with Product, Sales, Fleet, Service, Finance, and engineering teams; communicate estimates, dependencies, and risks clearly

Job Requirements

• Degree in Computer Science, Engineering, Physics, Math, or equivalent evidence of exceptional ability, or equivalent experience
• 6+ years of professional software engineering experience, including substantial ownership of production backend systems at scale
• Proven expertise designing, building, and operating distributed systems: microservices or SOA, concurrency, consistency, fault tolerance, and graceful degradation
• Expert-level proficiency in at least one backend language used in large systems (Go, Java, Python, C#, or equivalent), including production debugging of complex runtime issues
• Deep, hands-on experience with Kubernetes and containerized workloads, including deployment, scaling, resource management, and production operations
• Experience operating high-traffic or high-throughput systems, including load, queueing, rate limiting, caching, and performance profiling
• Strong data layer experience: relational databases (e.g. MySQL/PostgreSQL) plus at least one of Redis, Kafka/event streaming, or OpenSearch/Elasticsearch
• Practical experience building agentic AI workflows to improve operational efficiency, including knowledge of the agentic loop (plan, tool use, observation, retry/correction) and grounding agents in reliable backend systems
• Experience building AI-based anomaly detection on production signals (metrics, logs, events, or operational data), and daily use of AI coding agents to accelerate design, implementation, review, and debugging
• Clear communication and high agency; automotive, remarketing, fleet, logistics, or other high-volume physical-goods operations experience is a plus