Agentic AI Harness Architect - Moveworks

About the job

Responsibilities

• Set the technical vision for the agentic AI harness, including planning, tool selection, context management, memory, critique, reflection, adaptation, and recovery.
• Invent and prototype algorithms to help agents complete longer, more complex tasks with less supervision and stronger outcome reliability.
• Design model-agnostic abstractions for combining foundation models, specialized agents, enterprise tools, policies, and deterministic workflows.
• Develop approaches for agent memory and learning from execution traces, user feedback, and task outcomes while preserving enterprise security and privacy.
• Create rigorous benchmarks and evaluation methods for tool-use correctness, plan quality, task completion, groundedness, policy compliance, latency, and cost.
• Explore methods such as iterative planning, ReAct-style execution, self-critique, reflection, test-time computation, multi-agent coordination, and verifier-guided reasoning.
• Build high-fidelity prototypes, identify ideas that merit investment, and work with platform and product teams to define a practical path to production.
• Partner across teams to connect agent intelligence with enterprise knowledge, permissions, and actions.
• Remain hands-on with code and experiments while influencing architecture, mentoring senior engineers, and raising the technical bar.

Requirements

• Deep expertise in machine learning or artificial intelligence, paired with strong systems judgment and the ability to reason about end-to-end product architecture.
• A record of meaningful work on AI agents, reasoning and planning, tool use, coding agents, conversational systems, reinforcement learning, or closely related areas.
• Experience turning ambiguous research questions into working prototypes, measurable hypotheses, and durable technical direction.
• Strong understanding of the tradeoffs among model capability, context, memory, latency, cost, reliability, and system complexity.
• Experience designing evaluations for probabilistic systems, including benchmarks that measure task outcomes rather than surface-level response quality alone.
• Excellent programming skills and the ability to work directly in modern ML and agent stacks; Python expertise is expected.
• Evidence of technical leadership across teams, including the ability to create clarity, influence roadmaps, and guide other senior engineers.
• Typically 8 or more years of relevant industry or research experience, or an equivalent record of exceptional technical and research impact.

Benefits

• Base pay of $201,300 - $352,300
• Equity (when applicable)
• Variable/incentive compensation
• Health plans, including flexible spending accounts
• 401(k) Plan with company match
• ESPP
• Matching donations
• Flexible time away plan
• Family leave programs