Responsibilities
About the Team:
Site Reliability Engineering (SRE) of the AML (Applied Machine Learning) team combines system engineering and the art of machine learning to develop and run a massively distributed AI/ML recommendation system for the United States and all around the world.
On the SRE team, you'll have the opportunity to sharpen your expertise in coding, performance analysis, and large-scale systems operation. 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.
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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.
Qualifications
Minimum 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 PaddlePaddle