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Trulioo, a global leader in digital identity verification, is seeking a Staff Engineer or Architect to design, build, and own AI agent systems end-to-end for identity, fraud, risk, and commerce workflows. You will report to the Director of Software Engineering and take ownership from design through production, including evaluation harnesses and observability.
Key responsibilities include:
- Design and build reliable, steerable, multi-step agent workflows that plan, execute, observe outcomes, and iteratively improve, including tool use and human-in-the-loop patterns for regulated, high-stakes decisions.
- Troubleshoot novel, non-deterministic failures across models, tools, orchestration, and infrastructure, staying calm under ambiguity and finding root causes through hypothesis-driven investigation.
- Work within a compliance-heavy, globally regulated domain where auditability, correctness, and human oversight are critical features.
- Set and communicate technical vision to teammates and the agents you direct, making implicit requirements explicit so both humans and agents can execute reliably.
- Leverage AI and emerging technologies to improve productivity and streamline workflows while ensuring responsible, secure, and compliant use.
- Orchestrate AI agents in your daily work, maintaining a clear mental model of the system, verifying changes, and staying accountable.
This is a full-time, permanent position based in San Diego on a hybrid model (3 days per week in office) or fully remote within North America. Trulioo is headquartered in Vancouver with strategic hubs in San Diego and Dublin, and offers a hybrid working environment with comprehensive benefits including health/dental/vision coverage, retirement plans with company match, paid time off, parental leave, and an annual education & training stipend ($1,000 equivalent in local currency).
Requirements:
- 8+ years of software engineering experience with demonstrated impact on production systems.
- Fluency using AI agents in your own engineering workflow; ability to show how you maintain a mental model, verify outputs, and stay accountable while moving faster with AI.
- Demonstrated ability to own features end-to-end and communicate trade-offs clearly to both technical and non-technical stakeholders.
- Experience working on challenging, novel, or ambiguous projects where you defined the problem, not just solved a handed-down spec.
- Hands-on experience building with LLMs: prompt engineering, RAG, tool use, and at least one agent framework (e.g., LangGraph, CrewAI, AutoGen, or similar), plus evaluation frameworks for agent behavior.
Nice to have:
- Experience in financial services, identity, fraud, or risk domains, or other compliance-heavy environments (AML, fraud rules engines, identity graphs, risk scoring).
- Multi-agent orchestration patterns and production-grade evaluation harnesses.
- AI observability tooling; MLOps practices (experiment tracking, model registries, feature stores).
- Vector databases, knowledge graphs, or structured retrieval for agent memory.
