Senior Platform Engineer - USDS

About the role

The Cyber Defense & Engineering team is missioned to run and operate security infrastructures, platforms and technologies, as well as to support cross-functional teams to protect our users, products and infrastructures. This team is responsible for enhancing security tools and identifying vulnerabilities, with a specific focus on content assurance and the application of large language models (LLMs).

You'll collaborate cross-functionally with partners inside and outside TikTok to fortify our products and users' security, helping to establish TikTok as the most trusted platform.

Responsibilities

• Architect and operate highly available, on-premise Kubernetes-based GPU compute cluster. You will manage the scheduling and orchestration of high performance workloads.
• Build robust CI/CD pipelines specifically for LLM applications.
• Infrastructure as Code (IaC): Lead the design and implementation of IaC (using Terraform, Ansible or Saltstack) to fully automate the provisioning bare metal servers, network and storage layers, and ensuring the environment is reproducible and idempotent.
• Lead and perform hands-on technical work, including architecture design and code development for an on-premise, highly scalable, and parallelized infrastructure. The role includes developing internal tools to manage the entire lifecycle of a large scale RAG pipeline.
• Architect, implement, and manage a high-performance compute cluster for LLM workloads. This involves the selection and configuration of specialized hardware like GPUs, as well as the design of a robust network fabric to facilitate efficient inter-node communication for parallel processing.
• Implement security best practices for a private data center environment. This includes configuring network firewalls, managing access controls, and encrypting data at rest and in transit.
• Establish comprehensive monitoring and alerting systems to track the health and performance of the compute cluster and LLM workloads. This involves analyzing metrics related to GPU utilization, memory usage, network throughput, and model inference latency. You will proactively resolve performance issues to enhance platform reliability and operational support for internal teams.
• Collaborate with internal stakeholders to optimize resource utilization and improve the platform's efficiency. You'll work closely with data scientists and machine learning engineers to understand their compute needs and ensure the infrastructure is optimized for their specific workloads.

Qualifications

• Bachelor's degree in Computer Science, Information Technology, or a related field, with 5+ years of experience in platform, systems, or infrastructure engineering.
• Proven expertise in infrastructure automation using tools like Terraform, Ansible or Saltstack, with strong hands-on experience in automating deployments and managing bare-metal hardware and virtual machines.
• Deep experience with on-premises infrastructure, with a solid understanding of large scale data processing, distributed computing and other big data infrastructure.
• Strong grasp of networking, storage, and virtualization technologies, with practical knowledge of building and supporting complex distributed systems for parallel processing of LLM workloads.
• Hands-on experience with container technologies (e.g., Docker) and managing on-premises Kubernetes clusters in production environments.
• Proficiency in scripting and automation using languages like Python and Go, and experience with full-stack development in languages such as Rust to build internal platforms.