Tech Lead-Machine Learning Engineer (Agent & Multi-Agent Systems) – AIGC Risk Intelligence

TikTok
Seattle
Workplace: OnsiteFull timeFunction: Data Science & Machine LearningExperience: 5+ yearsSkills: []

Build AI-native risk intelligence systems that identify emerging AIGC-driven threats. Lead the transition from monolithic LLM apps to structured multi-agent architectures with ReAct-style tool-augmented reasoning, modular skill composition, and execution traceability/observability. Design memory architectures and orchestration layers for vertical domain agents, implement feedback-driven optimization loops, and establish engineering standards for robustness, scalability, and production readiness while partnering with Risk, Safety, Infra, and ML teams.

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FursaFursa
TikTok
TikTok
1 month ago

Tech Lead-Machine Learning Engineer (Agent & Multi-Agent Systems) – AIGC Risk Intelligence

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Source: Company careers pageValidated by: Fursa AI
Last checked: 8 hours agoStatus: Live

Job Summary

Build AI-native risk intelligence systems that identify emerging AIGC-driven threats. Lead the transition from monolithic LLM apps to structured multi-agent architectures with ReAct-style tool-augmented reasoning, modular skill composition, and execution traceability/observability. Design memory architectures and orchestration layers for vertical domain agents, implement feedback-driven optimization loops, and establish engineering standards for robustness, scalability, and production readiness while partnering with Risk, Safety, Infra, and ML teams.
Location: Seattle
Workplace: Onsite
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Sr. Manager level

Key Responsibilities

  • •Architect and implement structured agent workflows (e.g., Evaluate → Validate → Reflect → Summarize) with ReAct-style tool allocation and robust memory architectures.
  • •Lead multi-agent architecture development by designing orchestration layers, modular skill systems, and execution graph/planning abstractions with debugging and auditability traceability.
  • •Develop open risk detection capabilities to identify previously unseen risk patterns using execution-trace-driven optimization loops and feedback signals.
  • •Establish engineering standards for agent systems, including traceability, observability, guardrails, and evaluation/integration of multi-agent frameworks for production readiness.
  • •Provide technical leadership by owning the roadmap, partnering with Risk, Safety, Infra, and ML teams, and mentoring engineers to drive architectural rigor.

Key Requirements

  • •5+ years of experience in software engineering or applied AI systems.
  • •Deep understanding of LLM-based agent architectures, including ReAct-style reasoning/tool calling, workflow orchestration, and memory design patterns.
  • •Experience designing distributed or modular AI systems.
  • •Strong backend engineering skills (Python or equivalent).
  • •Experience operating in adversarial or high-stakes environments.
Experience:5+ years
Tech Stack:PythonLLMsReActTool callingWorkflow orchestrationMemory architecturesRLPolicy optimizationExecution trace loggingObservability

Company Brief

TikTok
Short-form video platform that lets users create, share, and discover entertainment content through algorithmic recommendations. It also offers advertising and creator tools for brands, influencers, and businesses.
Industry: Digital Media
Company Size: Enterprise (1,001+ employees)
Growth: Scaleup
Headquarters: Singapore, Singapore
Founded: 2016
WebsiteLinkedIn