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

Responsibilities

Our team is building the next generation of AI-native risk intelligence systems to address emerging challenges driven by large-scale AIGC content production.
As content creation becomes automated and adversaries adopt systematic experimentation (e.g., large-scale template variation and rapid iteration), traditional rule-based and single-model approaches are no longer sufficient. We are transitioning from monolithic LLM applications to a structured multi-agent architecture that emphasizes:
Tool-augmented reasoning (ReAct-style systems)
Modular skill composition
Execution traceability and observability
Feedback-driven system evolution
Cross-domain risk reasoning
We are seeking an experienced technical leader to define and implement this architecture.

Responsibilities
- Architect and Implement Agent Systems
Design structured agent workflows (e.g., Evaluate → Validate → Reflect → Summarize)
Implement ReAct-style tool allocation and reasoning frameworks
Develop short-term and long-term memory architectures
Ensure robustness under adversarial and evolving conditions

- Lead Multi-Agent Architecture Development
Design orchestration layers for coordinating vertical domain agents
Build modular Skill systems for extensibility and reuse
Define execution graph standards and planning abstractions
Establish traceability mechanisms for debugging and auditability

- Develop Open Risk Detection Capabilities
Architect systems capable of identifying previously unseen risk patterns
Implement execution trace-driven optimization loops
Translate feedback signals (FP/FN, reviewer overrides, drift signals) into system improvements
Enable proactive rather than purely reactive detection systems

- Establish Engineering Standards for Agent Systems
Define traceability, observability, and guardrail requirements
Evaluate and integrate multi-agent frameworks where appropriate
Ensure production-readiness, scalability, and reliability

- Provide Technical Leadership
Own the technical roadmap for Agent and multi-agent systems
Partner cross-functionally with Risk, Safety, Infra, and ML teams
Mentor engineers and drive architectural rigor

Qualifications

Minimum Qualifications
- 5+ years of experience in software engineering or applied AI systems
- Deep understanding of LLM-based agent architectures (ReAct-style reasoning/Tool calling systems/Workflow orchestration/Memory design patterns)
- Experience designing distributed or modular AI systems
- Strong backend engineering skills (Python or equivalent)
- Experience operating systems in adversarial or high-stakes environments

Preferred Qualifications
- Experience in trust & safety, risk detection, or adversarial ML
- Familiarity with multi-agent orchestration frameworks
- Experience building systems with execution trace logging and observability
- Background in RL-style policy optimization or iterative system refinement
- Demonstrated experience leading high-impact technical initiatives

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $241680 - $456000 annually.

Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.

Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).

The Company reserves the right to modify or change these benefits programs at any time, with or without notice.

For Los Angeles County (unincorporated) Candidates:

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:

1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;

2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and

3. Exercising sound judgment.

Responsibilities

Responsibilities

Our team is building the next generation of AI-native risk intelligence systems to address emerging challenges driven by large-scale AIGC content production.
As content creation becomes automated and adversaries adopt systematic experimentation (e.g., large-scale template variation and rapid iteration), traditional rule-based and single-model approaches are no longer sufficient. We are transitioning from monolithic LLM applications to a structured multi-agent architecture that emphasizes:
Tool-augmented reasoning (ReAct-style systems)
Modular skill composition
Execution traceability and observability
Feedback-driven system evolution
Cross-domain risk reasoning
We are seeking an experienced technical leader to define and implement this architecture.

Responsibilities
- Architect and Implement Agent Systems
Design structured agent workflows (e.g., Evaluate → Validate → Reflect → Summarize)
Implement ReAct-style tool allocation and reasoning frameworks
Develop short-term and long-term memory architectures
Ensure robustness under adversarial and evolving conditions

- Lead Multi-Agent Architecture Development
Design orchestration layers for coordinating vertical domain agents
Build modular Skill systems for extensibility and reuse
Define execution graph standards and planning abstractions
Establish traceability mechanisms for debugging and auditability

- Develop Open Risk Detection Capabilities
Architect systems capable of identifying previously unseen risk patterns
Implement execution trace-driven optimization loops
Translate feedback signals (FP/FN, reviewer overrides, drift signals) into system improvements
Enable proactive rather than purely reactive detection systems

- Establish Engineering Standards for Agent Systems
Define traceability, observability, and guardrail requirements
Evaluate and integrate multi-agent frameworks where appropriate
Ensure production-readiness, scalability, and reliability

- Provide Technical Leadership
Own the technical roadmap for Agent and multi-agent systems
Partner cross-functionally with Risk, Safety, Infra, and ML teams
Mentor engineers and drive architectural rigor

Qualifications

Qualifications

Minimum Qualifications
- 5+ years of experience in software engineering or applied AI systems
- Deep understanding of LLM-based agent architectures (ReAct-style reasoning/Tool calling systems/Workflow orchestration/Memory design patterns)
- Experience designing distributed or modular AI systems
- Strong backend engineering skills (Python or equivalent)
- Experience operating systems in adversarial or high-stakes environments

Preferred Qualifications
- Experience in trust & safety, risk detection, or adversarial ML
- Familiarity with multi-agent orchestration frameworks
- Experience building systems with execution trace logging and observability
- Background in RL-style policy optimization or iterative system refinement
- Demonstrated experience leading high-impact technical initiatives