Description
Compensation
The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. The salary range for this position is $207K – $445K, with generous equity, performance-related bonus(es) for eligible employees, and the following benefits.
Benefits include:
- Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts
- Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)
- 401(k) retirement plan with employer match
- Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)
- Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees
- 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time
- Mental health and wellness support
- Employer-paid basic life and disability coverage
- Annual learning and development stipend to fuel your professional growth
- Daily meals in our offices, and meal delivery credits as eligible
- Relocation support for eligible employees
- Additional taxable fringe benefits, such as charitable donation matching and wellness stipends
About the Team
The Product & Platform teams at OpenAI are responsible for delivering the company’s most impactful offerings,such as ChatGPT, our API platform, and new enterprise capabilities,to a global and diverse customer base. The ChatGPT Multimodal team works across voice, image generation, and other multimodal experiences to turn frontier research capabilities into reliable products.
About the Role
We are seeking a Technical Program Manager 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, and complex cross-functional dependencies for voice and image-generation launches.
Responsibilities
- 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, with clear 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, including system prompts, sampling behavior, multimodal configurations, inference differences, and other sources of 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 the 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 that multimodal launches depend on, including multimodal actor recruitment and selection and voice-related product partnerships across vehicles, smart speakers, and headphone ecosystems.
- Create clear operating cadences, decision rights, metrics, risk management, and executive-ready communication across complex, time-sensitive programs.
Requirements
- Have led technically complex programs across machine learning, multimodal products, model evaluation, data pipelines, inference, capacity, or large-scale product infrastructure.
- Can move fluently between user-facing product behavior and the underlying model, evaluation, configuration, serving, and capacity systems.
- Have built mechanisms that convert production signals into prioritized evaluations, data, engineering work, and measurable product improvements.
- Can reason credibly about GPU demand, serving constraints, latency, reliability, quality, and launch tradeoffs.
- Have closed research-to-production gaps and can drive structured debugging and validation across teams with different environments and incentives.
- Turn manual or fragmented workflows into scalable tooling, clear ownership, and durable operating practices.
- Lead effectively across research, engineering, product, operations, Human Data, and external partners without relying on direct authority.
- Communicate crisply, make ambiguity tractable, and use metrics and evidence to drive decisions.
- Thrive in ambiguous, scaling environments and can bring order to complex cross-functional work without losing pace.
- Care about OpenAI's mission and about expanding responsible access to advanced AI systems.