Manager, Applied AI Engineering (Enterprise)

OpenAI•New York, NY

18h•$251,000 - $335,000

About The Position

The Technical Success team at OpenAI helps customers derive significant and lasting value from our technology. We collaborate with customers throughout their entire journey, from initial exploration and solution design to production implementation and organization-wide adoption. Applied AI Engineers act as trusted technical advisors to customer executives, engineering teams, product leaders, security organizations, and transformation teams. They combine deep technical expertise with strong customer understanding, translating advanced AI capabilities into secure, reliable systems that deliver lasting business results. We are looking for a Manager to build, lead, and develop a high-performing team of Applied AI Engineers who will support our enterprise customers. You will be responsible for the technical success of a diverse and strategically important customer portfolio. Your role will involve helping your team identify high-value opportunities, design and deploy production-grade AI systems, navigate complex technical and organizational challenges, and scale successful implementations across various workflows, teams, and business units. This position demands technical depth, people leadership skills, customer insight, and operational excellence. You should be comfortable guiding engineers through architectural and evaluation decisions, engaging directly in critical customer situations, and collaborating effectively with Sales, Solutions Engineering, Product, Research, Engineering, Security, and Legal teams. Additionally, you will contribute to defining how we serve enterprise customers at scale by developing effective coverage models, reusable implementation patterns, technical enablement strategies, escalation procedures, and systems for channeling field insights into high-quality product feedback.

Requirements

Significant experience managing customer-facing technical teams, such as Applied AI Engineers, Solutions Architects, Forward Deployed Engineers, Customer Engineers, or Technical Account Managers.
Experience building or leading teams responsible for implementing complex software, data, machine learning, or AI systems in enterprise environments.
Sufficient technical depth to evaluate architectures, ask incisive questions, challenge assumptions, and coach engineers through difficult implementation decisions.
Experience taking AI, machine learning, or other technically complex systems from prototype to production.
Understanding of production-system requirements, including reliability, observability, security, privacy, data governance, evaluation, and operational readiness.
Experience leading teams through ambiguity, competing priorities, escalations, and rapidly evolving products or markets.
Ability to translate effectively among technical details, customer needs, product strategy, and business outcomes.
Experience working with large organizations involving multiple business units, stakeholder groups, procurement processes, or governance requirements.
Strong executive presence and ability to build trust with engineering leaders, business executives, security teams, and other senior stakeholders.
Experience designing operating models, coverage strategies, prioritization frameworks, or repeatable delivery processes for a growing technical organization.
Strong cross-functional partnership skills, with the ability to navigate disagreement directly while maintaining trust and shared accountability.
Ability to use data and clear principles to allocate limited resources across a large portfolio of opportunities.
A track record of coaching team members, raising performance, and building inclusive teams.
Enthusiasm for helping organizations adopt frontier AI responsibly and turn emerging capabilities into durable value.

Nice To Haves

Leading teams in applied AI engineering, solutions architecture, forward-deployed engineering, professional services, technical consulting, or customer engineering.
Implementing generative AI, machine learning, developer platforms, cloud infrastructure, data platforms, or other complex enterprise technologies.
Working with customers in semiconductors, media, entertainment, or similarly complex and technology-intensive industries.
Supporting global customers, multi-business-unit implementations, regulated workflows, or large-scale organizational transformations.
Building technical enablement, reusable solution patterns, evaluation frameworks, or customer implementation methodologies.
Direct experience in these industries is not required. We value leaders who can recognize common technical and organizational patterns while adapting their approach to different customer environments.

Responsibilities

Build, manage, and develop a high-performing team of Applied AI Engineers supporting large and complex enterprise customers.
Own the quality and impact of the team’s work across solution design, implementation, production readiness, adoption, and expansion.
Coach the team through decisions involving architecture, model selection, evaluations, reliability, latency, safety, security, governance, and cost.
Establish an operating model for prioritizing accounts and engagements according to customer needs, strategic value, technical complexity, and potential for repeatable impact.
Serve as a senior technical escalation point during critical launches, production incidents, complex integrations, and high-stakes customer decisions.
Partner with customer executives and technical leaders to connect implementation decisions to measurable business and operational outcomes.
Help customers progress from experimentation to production systems and sustained adoption across their organizations.
Partner closely with Sales and Solutions Engineering to create continuity throughout the customer lifecycle and maintain shared accountability for customer success.
Translate customer needs and recurring implementation challenges into actionable feedback for Product, Research, Engineering, Security, and other internal teams.
Identify scalable market patterns, distinguish them from bespoke requests, and advocate for investments that can benefit multiple customers.
Develop reusable architectures, evaluation methods, playbooks, tooling, and enablement that improve time to value across the enterprise portfolio.
Establish mechanisms for measuring production implementations, adoption, customer outcomes, delivery quality, team capacity, and the impact of reusable work.
Hire thoughtfully, raise the technical and leadership bar, and foster a culture of accountability, curiosity, collaboration, inclusion, and continuous learning.
Represent OpenAI with credibility and sound judgment in conversations with senior customer and internal stakeholders.