特斯拉后端开发工程师，辅助驾驶 Sr. Backend Software Engineer, Autopilot

任职要求

Strong proficiency in at least one backend/data engineering language (Python required; C++ or Go is a strong plus) with solid systems programming skills.
Experience with large-scale data processing (Spark, Pandas, NumPy, etc.) and real-time or near real-time data pipelines.
Familiarity with cloud-native technologies including Kubernetes, Terraform, Linux systems, networking, and storage.
Hands-on experience with databases and search systems (PostgreSQL, Redis, Elasticsearch, DynamoDB, etc.)
Experience with public cloud services (AWS EC2, S3, RDS, etc.).
Strong foundation in distributed systems design with focus on security, scalability, …

工作职责

THE ROLE
As a key member of the Autopilot AI Tooling & Teleoperation team, you will work on two critical areas:

Designing and building tooling systems that support the full machine learning lifecycle (data processing, annotation, visualization, training, and productionization pipelines);
Developing high-reliability, low-latency backend infrastructure to enable remote operation and assistance for Tesla’s global Robotaxi fleet and Optimus humanoid robots.

You will collaborate closely with world-class AI researchers, autonomy teams, firmware, and controls engineers to build an end-to-end technology stack — from data to cloud, and from model training to real-time teleoperation. Your work will directly impact the safety and continuous improvement of thousands of autonomous vehicles and robots.

RESPONSIBILITIES
Design and implement large-scale data processing pipelines handling diverse data types including autonomous driving images, sensor data, human annotations, and teleoperation video/telemetry/control signals.
Build tools, metrics, dashboards, and automation platforms that accelerate the full cycle of model training, validation, and teleoperation workflows.
Design and develop scalable, high-performance backend infrastructure to support low-latency remote operation of thousands of vehicles and robots worldwide, with strong emphasis on safety and reliability.
Establish comprehensive observability systems (monitoring, metrics, and alerting) to achieve full visibility from edge devices to the cloud.
Collaborate with AI researchers, frontend engineers, firmware, and autonomy teams to rapidly turn research ideas into production-grade features.
Select and implement cloud-native technologies (Kubernetes, Terraform, etc.), build automated testing frameworks, and enable safe continuous deployment.