It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of joy. Backed by Greylock and Sequoia.
About the role
In your first year, you will establish the architecture and research roadmap for the agentic AI harness, deliver prototypes that improve performance on real enterprise tasks, create evaluations that make those gains measurable, and guide successful approaches into the Moveworks Reasoning Engine and broader AI platform. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- What You Bring
- 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
More about this role
What You Will Do
• Set the technical vision for the Moveworks agentic AI harness, including planning, tool selection, context management, memory, critique, reflection, adaptation, and recovery.
• Invent and prototype algorithms that 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 the ideas that merit investment, and work with platform and product teams to define a practical path to production.
• Partner across Agentic Systems, Search, Relevance, ML...
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