Original listing text, shown exactly as published by the company.
About the Role
We are looking for an AI Deployment Engineering Manager, Startups to lead and scale the Startups AI Deployment Engineering motion. This team helps high-growth startups move quickly from experimentation to production, unlock meaningful usage, and build durable technical partnerships with OpenAI.
This leader will operate in a high-velocity customer segment where founders, CTOs, and technical teams expect speed, judgment, and hands-on problem-solving. They will balance team leadership, technical depth, customer prioritization, and cross-functional influence across Sales, Product, Engineering, Research, and broader go-to-market teams.
In this role, you will define how OpenAI supports startup customers at scale: identifying where deep technical engagement can unlock outsized impact, building repeatable deployment mechanisms, and ensuring the team can serve a broad and dynamic customer base without losing quality or strategic focus.
In This Role, You Will
- Craft and continuously refine the strategic vision and operating model for the Startups AI Deployment Engineering team, aligning it with OpenAI’s broader company objectives and the evolving needs of high-growth startup customers.
- Lead, mentor, and grow a team of high-performing technical ICs supporting startup customers across AI-native, developer-led, and product-led companies.
- Help startups move from early experimentation to production usage by identifying technical blockers, advising on architecture, and driving practical paths to deployment.
- Partner closely with Sales to determine where technical engagement can accelerate adoption, production usage, and long-term account growth.
- Represent the technical voice of startup customers by synthesizing high-signal feedback, especially around developer experience, product gaps, deployment blockers, model performance, and emerging use cases.
- Translate recurring startup needs into repeatable playbooks, starter packs, reference architectures, internal tooling, and customer-facing assets that help the broader team move faster.
- Serve as a senior technical escalation point for priority startup customers, including founder-, CTO-, and technical executive-level conversations.
- Balance urgent customer needs with OpenAI’s broader product and platform priorities, especially when startups request niche features, bespoke support, or accelerated roadmap changes.
- Coach AI Deployment Engineers on technical quality, customer judgment, prioritization, executive communication, and cross-functional partnership.
- Partner across Sales, Product, Engineering, Research, and other GTM teams to improve how OpenAI supports startups from early adoption through scaled production usage.
You Might Thrive in This Role If You
- Have proven experience founding, scaling, or operating within early-stage startups, ideally in technical leadership roles where you owned both product or customer outcomes and technical execution.
- Have a strong track record of building and leading technical, customer-facing teams in high-growth, ambiguous, or startup-heavy environments.
- Bring strong technical depth across APIs, platform products, AI/ML systems, developer workflows, and production deployment considerations.
- Have experience creating scalable operating models, not just managing one-off customer escalations.
- Are comfortable working directly with founders, CTOs, technical executives, and highly technical ICs.
- Demonstrate strong judgment about when to go deep for a strategic customer versus when to build repeatable mechanisms for the broader segment.
- Can translate complex technical and product considerations into clear decisions, practical guidance, and customer-facing recommendations.
- Operate with speed, iteration, and pragmatic customer impact without sacrificing quality, safety, or OpenAI’s long-term platform strategy.
- Are personally committed to fostering the safe and beneficial development of AI.
Success in This Role May Look Like
- Increased startup account usage, including tokens, requests, and production workloads.
- A strong cadence of successful customer sprints and production-oriented technical engagements.
- High-quality customer stories, founder and CTO satisfaction, and referenceable startup wins.
- Creation of repeatable startup deployment patterns, starter packs, templates, and reference architectures.
- Strong Product and Research feedback loops informed by startup customer needs and emerging use cases.
- A healthy, high-performing team with strong prioritization, clear operating mechanisms, and the ability to scale coverage across a high-demand segment.
