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

Location:

Seattle

Employment Type:

Regular

Job Code: A244382A

• 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.

• Minimum: 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:
  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.

The base salary range for this position in the selected city is $232,560 - $427,500 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.

• 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.
• 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;
• 3. Exercising sound judgment.