Machine Learning Engineer - Machine Learning Infrastructure

Founded in 2012, ByteDance's mission is to inspire creativity and enrich life. With a suite of more than a dozen products, including TikTok, Helo, and Resso, as well as platforms specific to the China market, including Toutiao, Douyin, and Xigua, ByteDance has made it easier and more fun for people to connect with, consume, and create content. Why Join Us Creation is the core of ByteDance's purpose. Our products are built to help imaginations thrive. This is doubly true of the teams that make our innovations possible. Together, we inspire creativity and enrich life - a mission we aim towards achieving every day. To us, every challenge, no matter how ambiguous, is an opportunity; to learn, to innovate, and to grow as one team. Status quo? Never. Courage? Always. At ByteDance, we create together and grow together. That's how we drive impact - for ourselves, our company, and the users we serve. Join us. The mission of our AML team is to push the next-generation AI infrastructure and recommendation platform for the ads ranking, search ranking, live & ecom ranking in our company. We also drive substantial impact on core businesses of the company. Currently, we are looking for Machine Learning Engineer - Machine Learning Infrastructure to join our team to support and advance that mission. Responsibilities: * Responsible for the design and implementation of a global-scale machine learning system for feeds, ads and search ranking models. * Responsible for improving use-ability and flexibility of the machine learning infrastructure. * Responsible for improving the workflow of model training and serving, data pipelines, storage system and resource management for multi-tenancy machine learning systems. * Responsible for designing and developing key components of ML infrastructure and mentoring interns. Job requirements * Proficient in at least one programming language such as Go/Python in Linux environment, with excellent coding skills. * Familiar with open source distributed scheduling/orchestration/storage frameworks, such as Kubernetes (K8S), Yarn (Flink, MapReduce), Mesos, Celery, HDFS, Redis, S3, etc., with rich practical experience in machine learning system development. * Master the principle of distributed systems and participate in the design, development and maintenance of large-scale distributed systems. * Possess excellent logical analysis ability, able to perform reasonable abstraction and decomposition of business logic. * Have a strong sense of responsibility, good learning ability, communication ability and self-motivation, and be able to respond and act quickly. * Have good working document habits, and write and update work flow and technical documents in a timely manner as required.