1 day ago

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

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 11 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

Website LinkedIn

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TikTok

1 day ago

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

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 11 hours agoStatus: Live

Job Summary

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

Industry: Digital Media

Company Size: Enterprise (1,001+ employees)

Growth: Scaleup

Headquarters: Singapore, Singapore

Founded: 2016

Website LinkedIn