Engineering Manager, AI Tooling

Airtable•San Francisco, CA

2d•$281,000 - $365,700•Hybrid

About The Position

Airtable is the no-code app platform that empowers people closest to the work to accelerate their most critical business processes. More than 500,000 organizations, including 80% of the Fortune 100, rely on Airtable to transform how work gets done. Airtable is building the future of app creation: a platform where anyone can create powerful, flexible workflows and applications without needing to start from scratch. As AI becomes a core part of how customers build, automate, and scale their work in Airtable, we are investing deeply in first-party AI experiences that make Airtable more powerful, approachable, and useful for every team. The AI Tooling / Field Agents team is responsible for driving growth, adoption, and durable usage of Airtable-native AI through Field Agents and related product experiences. The team’s primary goal is maximizing first-party AI token usage by helping more customers discover, understand, trust, and successfully use Field Agents and other such tools in real workflows. This team sits within the Product Engineering organization. Adjacent teams are responsible for products like Omni and the Airtable MCP. This role will focus on Field Agents and AI Tools, with some light connective tissue to accessibility and platform-quality work supporting AI products in general. The team’s work ladders up to driving first-party AI usage as a broader metric. The team partners closely with Product, Design, Data Science, AI engineering, Billing, Services, Sales/CS, and other product teams to identify high-impact adoption opportunities, run experiments, and remove blockers that prevent customers from getting value from Field Agents and AI Tools. Please note: while we employ a hybrid working model at Airtable (flexible in working from the office or elsewhere), we are looking to hire candidates at this level who are based in San Francisco and open to coming into the office ~2-3 times/week for team collaboration.

Requirements

Experience managing and developing high-performing product engineering teams.
Strong product sense and experience shipping user-facing product experiences with measurable business or usage impact.
Experience building AI, ML, automation, developer tooling, workflow, or product-led-growth experiences.
Experience partnering closely with PM, Design, Data Science, and GTM teams.
Comfort using metrics, experimentation, and customer feedback to guide roadmap and execution decisions.
Strong technical judgment across full-stack product development; AI product experience is strongly preferred but not strictly required.
A track record of leading teams through ambiguity, changing priorities, and high-visibility goals.
Excellent communication skills, including the ability to create clarity for engineers, cross-functional partners, and leadership.
Strong execution instincts: you know when to run a lightweight experiment, when to invest in platform quality, and when to cut scope to learn faster.
Experience hiring, coaching, and developing senior engineers and technical leads.

Nice To Haves

Experience with experimentation platforms, growth loops, activation/adoption funnels, or usage-based business models.
Experience with billing, credits, metering, quota systems, or customer-facing usage transparency.
Experience working with enterprise customers, Sales/CS/Services partners, or complex customer adoption motions.
Accessibility experience or a strong appreciation for inclusive product quality.

Responsibilities

Lead, manage, and grow a team of engineers working on Field Agent Growth, AI tooling, and related adoption surfaces.
Own engineering execution for initiatives that increase Field Agent usage, first-party AI token consumption, and Agent WAU.
Partner with Product and Design to identify high-leverage growth opportunities, quickly validate hypotheses, and ship product experiences that help customers discover and successfully use Field Agents.
Drive a rigorous experimentation and rollout culture, including experiment architecture, ramp plans, exposure quality, launch readiness, metric instrumentation, and pre/post analysis.
Help the team balance growth-oriented product bets with defensive trust-building work such as credit transparency, spend visibility, warnings, and guardrails.
Work with Data Science and Product to understand usage patterns, token consumption, adoption funnels, and customer segments where Field Agents can create the most value.
Collaborate with Services, Sales, and Customer Success to identify real customer workflows and unblock enterprise adoption, especially where customers have conceptual, permissions, billing, performance, or AI-readiness barriers.
Provide technical guidance across full-stack product work, AI product surfaces, billing/credits integrations, experimentation systems, and high-quality end-user experiences.
Build a strong team operating model: clear DRIs, crisp PDC/status updates, effective sprint planning, high-quality execution reviews, and healthy collaboration across engineering, product, and design.
Develop engineers through feedback, coaching, career development, technical mentorship, and creating opportunities for ownership.