Site Reliability Engineer - Applied Machine Learning - USDS

DescriptionTeam introduction:Site Reliability Engineering (SRE) of the AML (Applied Machine Learning) team combines system engineering and the art of machine learning to develop and run massively distributed AI/ML systems like recommendation or LLM in the United States and around the world.In the SRE team, you'll have the opportunity to sharpen your expertise in coding, performance analysis, and large-scale system operation, and get heavily involved in the process of hardware/capacity decision-making. SRE ensures that critical machine learning systems have the highest level of availability, as well as creating highly automated systems and pipelines.Join us and you'll have the chance to shape the future of AML systems and make a real, tangible impact on TikTok users.Responsibilities:- Design, build, and maintain highly available, scalable, and fault-tolerant systems.- Monitor and analyze system performance, identifying and resolving issues before causing user impact.- Develop and maintain automated monitoring, alerting, and incident response systems.- Collaborate closely with software engineering teams to ensure that applications are designed with reliability, scalability, and performance in mind.- Implement and maintain security best practices and ensure compliance with regulatory requirements.- Participate in on-call rotations and respond to issues and incidents within and outside of normal business hours.- Conduct root cause analysis of incidents, hold post-mortem reviews with stakeholders, and implement preventative measures to minimize the risk of similar incidents occurring in the future.QualificationsMinimum Qualifications:- Expertise in analyzing and troubleshooting Linux-based distributed systems.- Bachelor's/Master's degree in Computer Science, Computer Engineering, or equivalent years of experience in a SRE or software engineering role.- Experience programming with at least one commonly used language (C, C++, Python, Go).- Strong understanding of data structures and algorithms.- Competent knowledge of relational database systems.Preferred Qualifications:- Ability to design and maintain large-scale systems.- Strong understanding of code optimization and routine task automation.- Proficiency in at least one machine learning framework: TensorFlow, PyTorch, MXNet or PaddlePaddleEmployment Type: Regular