The opportunity
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…
What you'll do
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.
Measure and validate improvements: Build diagnostic tools, instrumentation, representative device workloads, and automated tests to demonstrate repeatable performance and energy gains on physical devices, catch regressions, and validate sustained use.
What they're looking for
- Bring capabilities to production: Collaborate with research, hardware, firmware, platform, and product engineering teams to turn emerging model capabilities into maintainable systems and reliable shipped features.
- Substantial hands-on experience designing, developing, and debugging: operating system components, system services, or performance-critical platform software.
- Proficiency in C++ for systems development, including concurrent programming,: memory ownership, and resource lifetime management.
- Hands-on experience integrating or optimizing inference runtimes or machine: learning workloads in resource-constrained environments.
