Senior Director of AI, R&D & Agentic Systems

United StatesRemotesenior

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Quick Summary

Overview

About UsQuillBot was founded in 2017 with a mission to help students and professionals strengthen their writing. Today, we help over 56 million people around the world create great things.

Key Responsibilities

AI Architecture & Orchestration Design and lead systems for intent recognition, task decomposition, and multi-step execution across QuillBot’s product surfaces Define how agentic systems plan, coordinate, and execute workflows across a multi-surface…

Requirements Summary

AI & Systems Expertise Experience building and shipping agentic systems or orchestration platforms in production at meaningful scale, not prototypes or research demos Distributed Agency: deep expertise coordinating diverse agent populations…

Technical Tools

etl

Ship and iterate on production agentic workflows connecting QuillBot's core tools and surfaces, proving the orchestration architecture under real user load
Make high-conviction technical bets on the model stack, compute strategy, and agent execution model, with written rationale that the org can build against
Own the technical point of view on which ICP verticals and workflows justify agentic investment, and drive that conviction into AI, product, and engineering planning
Inherit and reshape the AI org to match the velocity and scope of what we're building, including hiring where gaps exist

Responsibilities

~1 min read

Design and lead systems for intent recognition, task decomposition, and multi-step execution across QuillBot’s product surfaces
Define how agentic systems plan, coordinate, and execute workflows across a multi-surface application suite
Establish standards for agent-native infrastructure, enabling product surfaces to be machine-readable and executable

Lead research and development across text and multimodal domains, with an initial focus on text and image capabilities
Build on QuillBot’s existing strengths in NLP while extending into new modalities to create differentiated product experiences
Make model strategy decisions across proprietary and third-party systems, balancing capability, cost, and shipping velocity

Drive the shipping cadence for model updates, MLOps, and data pipelines across high-scale production
Own system reliability and performance of the AI stack serving tens of millions of users worldwide
Balance system performance, latency, and cost through informed decisions on compute economics and architecture

Build and lead an integrated AI organization spanning R&D and Applied AI teams
Partner with the VP of AI and senior leadership to define technical direction, evaluate trade-offs, and guide platform-level decisions
Drive alignment across Engineering, Product, and Design to ensure adoption of agent-native standards
Raise the quality bar across the organization, acting as a force multiplier for team performance and execution
Architect the team's operating model around AI-native workflows, using automated pipelines, code generation, and internal tooling to achieve output disproportionate to headcount

Requirements

~1 min read

Experience building and shipping agentic systems or orchestration platforms in production at meaningful scale, not prototypes or research demos
Distributed Agency: deep expertise coordinating diverse agent populations (plan-based, scripted, and hybrid) within stateful environments, including task decomposition, intent routing, and multi-step execution
State & Persistence: proven ability to design and operate systems that maintain sustained, context-aware agency across multi-session and multi-domain workflows
Compute Economics: ability to optimize sophisticated planning logic against the constraints of latency, unit economics, and reliability at consumer scale
Background in NLP and/or multimodal AI (text and image preferred), with the ability to guide applied research, evaluate model architectures, and make binding technical decisions on model strategy across proprietary and third-party ecosystems

Proven leadership of both AI R&D and Applied AI/MLOps teams within consumer product environments serving millions of users
Track record of scaling AI systems and infrastructure from early-stage builds (0→1) into high-scale production (1→10), not just inheriting mature platforms
Forms strong technical opinions quickly, updates them based on evidence rather than consensus, and translates AI capabilities into product direction and business impact
Has built or reshaped AI organizations to match the demands of a rapidly evolving technical mandate, including hiring, restructuring, and raising the performance bar

Drives alignment across Engineering, Product, and executive stakeholders through technical credibility and strategic clarity, not positional authority
Operates effectively across global distributed teams and time zones

Nice to Have

~1 min read

Experience with agent-to-tool communication frameworks or emerging standards (e.g., MCP, agent SDKs)
Contributions to peer-reviewed research or involvement in the broader AI research community
Experience with browser-based or edge compute environments (e.g., WebGPU)
Background in creative, productivity, or prosumer software platforms
Experience operating in founder-led or highly entrepreneurial environments