Operating Systems Engineer, On-Device Inference | Consumer Devices
OpenAI
- Pay
- $230k–$385k
- Workplace
- On-site
- Location
- San Francisco · California · United States
- First seen
- 2 hours ago
- Last seen
- 2 hours ago
- Board
- Ashby
Summary
ABOUT THE TEAM OpenAI Consumer Devices is building the next generation of products that bring powerful AI into people’s everyday lives.
Posting
ABOUT THE TEAM OpenAI Consumer Devices is building the next generation of products that bring powerful AI into people’s everyday lives. Guided by OpenAI’s mission to ensure AGI benefits all of humanity, our team combines world-class researchers, engineers, designers, and operators who care deeply about creating useful, intuitive, and responsible technology. You’ll have the opportunity to work alongside exceptional people on ambitious, zero-to-one challenges at the intersection of hardware, software, and AI. This is a chance to help define an entirely new category of products—and shape how people experience AI in the future. Our team works across silicon, embedded systems, operating systems, and cloud services to build reliable consumer devices and the novel platforms required to support them. We partner closely with research to bring advanced AI capabilities into the physical world. ABOUT THE ROLE As an Operating Systems Engineer focused on on-device inference, you will design, develop, and ship the OS stack that makes advanced AI capabilities reliable, responsive, and energy efficient on consumer devices. Your work will span OS services and frameworks, inference runtime integration, model fitting, scheduling, and performance and power management. You’ll partner with research to adapt models to device constraints, make design decisions across the stack, and carry solutions from early exploration through integration and production. IN THIS ROLE, YOU WILL: - Build the inference platform: Design and implement maintainable OS services, frameworks, and clear interfaces for inference execution, model loading and lifecycle, and resource management. - Fit models to device constraints: Partner with researchers on quantization, runtime integration, and memory optimization to meet memory, compute, and energy budgets while evaluating model quality and product behavior. - Coordinate system resources: Develop scheduling and resource policies that balance inference with other device activity, preserving responsiveness within latency, memory, battery, and thermal constraints. - Advance performance and power management: Develop and validate execution strategies that adapt to workload needs, available resources, and changing device conditions. - Debug across the stack: Use tracing, profiling, and structured debugging to investigate correctness, concurrency, performance, and reliability issues across models, inference runtimes, and OS components. - Me