Site Reliability Engineer - AML Global Recommendation - USDS

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.
- 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