Job Description

About the Team

You will join the TikTok USDS Data Governance and Assurance Engineering team. We are the architects of the foundational infrastructure that manages the entire data lifecycle for one of the world's most massive data ecosystems. Our mission is to ensure that every byte of US data is handled with the highest standards of integrity, security, and privacy compliance.

Our team sits at the core of Privacy Engineering and Distributed Systems. We don't just write privacy policies; we build the automated engines that enforce them. We are replacing manual oversight with "Privacy-as-Code," ensuring that data privacy is a hard technical guarantee rather than a best-effort guideline.

About the Role

As a Software Engineer - AI Safety, you are the builder of the platform that makes AI assurance possible. You aren't just supporting ML; you are architecting the control plane that governs how data flows into models and how model outputs are verified. You will build high-scale systems that integrate AI models (LLMs, Classification models) to automate compliance and data lifecycle management at a petabyte scale.

Core Focus Areas:

- Safety Control Plane: Building the "mission control" for AI safety, allowing for real-time policy updates and model-interventions across heterogeneous environments.

- Automated Data Lifecycle: Engineering the systems that manage data inventory, lineage, and residual scanning to ensure training data meets strict privacy and purpose-limitation protocols.

- AI-Powered Governance: Integrating AI models into the governance workflow to automate the detection of non-compliant data or unsafe model "promotions" in real-time.

Responsibilities

- Assurance Platform Engineering: Design and scale the core platforms that orchestrate AI safety checks, ensuring that every recommendation signal is auditable and compliant with USDS standards.

- Real-time Interception Systems: Architect low-latency "circuit breakers" and middleware that sit within the recommendation path to enforce safety guardrails without impacting user experience.

- High-Scale Data Governance: Build distributed systems for automated data lineage and "right-to-be-forgotten" (RTBF) workflows, ensuring that user data is handled correctly throughout the ML training and inference lifecycle.

- Model Integration & Orchestration: Develop the backend services that allow for seamless integration of safety models (e.g., toxicity classifiers, LLM evaluators) into the broader TikTok infrastructure.

- Observability & Risk Telemetry: Build centralized dashboards and alerting engines that provide real-time visibility into the "safety health" of our recommendation and GPAI systems.