Technical Program Manager, Multimodal
OpenAI · San Francisco
About this role
## About the Team OpenAI’s Product & Platform teams deliver the company’s most impactful offerings—such as ChatGPT, the API platform, and new enterprise capabilities—to a global and diverse customer base. These systems must perform at scale and provide exceptional experiences for developers, consumers, and businesses. The **ChatGPT Multimodal** team works across **voice, image generation, and other multimodal experiences** to turn frontier research capabilities into reliable products. The team connects product usage and failure patterns with **research, evaluation, data, inference, capacity, and external partnerships** so that model and product improvements translate into better user experiences. ## About the Role OpenAI is seeking a **Technical Program Manager (TPM)** to build the “flywheel” that helps **ChatGPT multimodal products learn from real-world usage and improve quickly**. You will lead programs spanning: - Production-signal mining - Evaluation and data pipelines - Research-to-production parity - Multimodal capacity planning - Complex cross-functional dependencies for **voice and image-generation** launches You’ll work closely with **product engineering, research, Human Data, inference and capacity teams, safety partners, and external vendors/product partners**. This role is **based in San Francisco, CA** with a **hybrid model (3 days in the office per week)** and **relocation assistance**. ## In This Role, You Will - Build a system for mining production conversations and product signals to identify representative multimodal workflows, user needs, and failure modes. - Establish and maintain evaluations for the highest-priority multimodal behaviors and use cases, including coverage, quality standards, and ownership. - Package production signals into decision-ready data and evaluations that research teams can use to improve model behavior. - Measure whether model, prompt, configuration, and product changes produce meaningful improvements in multimodal evaluations and user outcomes. - Close gaps between research and production environments (e.g., system prompts, sampling behavior, multimodal configurations, inference differences) to reduce parity drift. - Create a repeatable process for reproducing product failures with research partners and validating fixes in the shipped experience. - Lead multimodal capacity planning by forecasting demand, translating it into **GPU and serving needs**, and managing headroom and reallocation tradeoffs for voice and image-generation workloads. - Improve tooling and operating processes used to plan, launch, and operate multimodal capabilities as demand and model behavior evolve. - Coordinate targeted multilingual data collection across research, Human Data, and external vendors. - Drive cross-functional programs required for multimodal launches, including multimodal actor recruitment/selection and voice-related partnerships across vehicles, smart speakers, and headphone ecosystems. - Define operating cadences, decision rights, metrics, risk management, and executive-ready communication across complex, time-sensitive programs. ## You Might Thrive If You - Have led technically complex programs across ML, multimodal products, model evaluation, data pipelines, inference, capacity, or large-scale product infrastructure. - Can move fluently between user-facing behavior and underlying systems (model, evaluation, configuration, serving, capacity). - Have built mechanisms that convert production si
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