小米Backend Software Engineer(DBA)

THE ROLE Tesla Insurance is a key business, offering innovative, data-driven solutions tailored to Tesla and non Tesla vehicle owners. By leveraging real-time vehicle data, AI, and cutting-edge technology, we provide fair, efficient coverage that enhances safety and customer satisfaction. As a Backend Software Engineer on our Ownership team, you'll join a dynamic, collaborative group of product-minded engineers passionate about building a seamless experience through scalable systems for quotes, policies, rating, underwriting, billings, AI agents, mobile and internal applications for customers and insurance agents. In this fast-paced environment, expect a blend of greenfield development, system enhancements, and strategic contributions to our architecture, with opportunities to integrate AI for smarter decision-making and drive direct impact on business growth and user experiences. We value high ownership and agency, empowering you to innovate while scaling our systems. RESPONSIBILITIES • Work closely with a wide variety of stakeholders to analyze and break down large requirements into small, simple, workable deliverables. • Work across multiple projects simultaneously. • Contribute to the complete software development lifecycle for applications, ensuring that all projects are delivered on time and meet high-quality standards • Design, develop, and maintain full stack web applications, utilizing .NET, C# and golang • Collaborate with cross-functional teams, including product managers, service engineers and other software engineers, to define and implement features that enhance operational efficiency for our customers • Ensure that web applications integrate seamlessly with backend distributed systems and support real-time monitoring, control, and reporting • Contribute to the definition and implementation of extensible design systems that prioritize scalability, maintainability, and exceptional user experiences • Implement rigorous testing strategies, including unit, integration, and end-to-end testing, to guarantee the reliability and performance of applications under various conditions • Actively maintain and promote the core values of the team, which include engineering excellence, curiosity, a bias for action, self-awareness, inclusivity, and vulnerability, contributing to a positive and productive team culture • Drive initiatives from concept to completion, from defining the business need with product experts, to conducting user interviews, culminating in feature implementation

THE ROLE Tesla Insurance is a key business, offering innovative, data-driven solutions tailored to Tesla and non Tesla vehicle owners. By leveraging real-time vehicle data, AI, and cutting-edge technology, we provide fair, efficient coverage that enhances safety and customer satisfaction. As a Backend Software Engineer on our Ownership team, you'll join a dynamic, collaborative group of product-minded engineers passionate about building a seamless experience through scalable systems for quotes, policies, rating, underwriting, billings, AI agents, mobile and internal applications for customers and insurance agents. In this fast-paced environment, expect a blend of greenfield development, system enhancements, and strategic contributions to our architecture, with opportunities to integrate AI for smarter decision-making and drive direct impact on business growth and user experiences. We value high ownership and agency, empowering you to innovate while scaling our systems. RESPONSIBILITIES • Work closely with a wide variety of stakeholders to analyze and break down large requirements into small, simple, workable deliverables. • Work across multiple projects simultaneously. • Contribute to the complete software development lifecycle for applications, ensuring that all projects are delivered on time and meet high-quality standards • Design, develop, and maintain full stack web applications, utilizing .NET, C# and golang • Collaborate with cross-functional teams, including product managers, service engineers and other software engineers, to define and implement features that enhance operational efficiency for our customers • Ensure that web applications integrate seamlessly with backend distributed systems and support real-time monitoring, control, and reporting • Contribute to the definition and implementation of extensible design systems that prioritize scalability, maintainability, and exceptional user experiences • Implement rigorous testing strategies, including unit, integration, and end-to-end testing, to guarantee the reliability and performance of applications under various conditions • Actively maintain and promote the core values of the team, which include engineering excellence, curiosity, a bias for action, self-awareness, inclusivity, and vulnerability, contributing to a positive and productive team culture • Drive initiatives from concept to completion, from defining the business need with product experts, to conducting user interviews, culminating in feature implementation

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.

Team Introduction: Data AML is ByteDance's machine learning middle platform, providing training and inference systems for recommendation, advertising, CV (computer vision), speech, and NLP (natural language processing) across businesses such as Douyin, Toutiao, and Xigua Video. AML provides powerful machine learning computing capabilities to internal business units and conducts research on general and innovative algorithms to solve key business challenges. Additionally, through Volcano Engine, it delivers core machine learning and recommendation system capabilities to external enterprise clients. Beyond business applications, AML is also engaged in cutting-edge research in areas such as AI for Science and scientific computing. Research Project Introduction: Large-scale recommendation systems are being increasingly applied to short video, text community, image and other products, and the role of modal information in recommendation systems has become more prominent. ByteDance's practice has found that modal information can serve as a generalization feature to support business scenarios such as recommendation, and the research on end-to-end ultra-large-scale multimodal recommendation systems has enormous potential. It is expected to further explore directions such as multimodal cotraining, 7B/13B large-scale parameter models, and longer sequence end-to-end based on algorithm-engineering CoDesign. Engineering research directions include: Representation of multimodal samples Construction of high-performance multimodal inference engines based on the PyTorch framework Development of high-performance multimodal training frameworks Application of heterogeneous hardware in multimodal recommendation systems 1. Algorithmic research directions include: 2. Design of reasonable recommendation-advertising and multimodal cotraining architectures 3. Sparse Mixture of Experts (Sparse MOE) 4. Memory Network 5. Hybrid precision techniques 团队介绍： Data AML是字节跳动公司的机器学习中台，为抖音/今日头条/西瓜视频等业务提供推荐/广告/CV/语音/NLP的训练和推理系统。为公司内业务部门提供强大的机器学习算力，并在这些业务的问题上研究一些具有通用性和创新性的算法。同时，也通过火山引擎将一些机器学习/推荐系统的核心能力提供给外部企业客户。此外，AML还在AI for Science，科学计算等领域做一些前沿研究。 课题介绍： 大规模推荐系统正在越来越多的应用到短视频、文本社区、图像等产品上，模态信息在推荐系统中的作用也越来越大。 字节实践中发现模态信息能够很好的作为泛化特征支持推荐等业务场景，端到端的超大规模多模态推荐系统的研究具有非常大的想象空间。 期望在算法和工程CoDesign基础上，对多模态Cotrain、7B/13B大规模参数模型、更长序列端到端等方向进一步进行探索。 工程上研究方向包括多模态样本的表征、基于 pytorch 框架的高性能多模态推理引擎、高性能多模态训练框架的构建、异构硬件在多模态推荐系统上的应用；算法上的研究方向包括设计合理的推荐广告和多模态Cotrain结构、Sparse MOE、Memory Network、混合精度等。 1、负责机器学习系统架构的设计开发，以及系统性能调优； 2、负责解决系统高并发、高可靠性、高可扩展性等技术难关； 3、覆盖机器学习系统多个子方向领域的工作，包括：资源调度、任务编排、模型训练、模型推理、模型管理、数据集管理、工作流编排、ML for System等； 4、负责机器学习系统前瞻技术的调研和引入，比如：最新硬件架构、异构计算系统、GPU优化技术的引入落地； 5、研究基于机器学习方法，实现对集群/服务资源使用情况的分析和优化。