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 $230K – $385K, with generous equity, performance-related bonuses for eligible employees, and the following benefits:
- 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 (1 hour per 30 hours worked, or more, as required by applicable state or local law)
- 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, may also be provided.
About the Team
OpenAI Consumer Devices is building the next generation of products that bring powerful AI into people's everyday lives. 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.
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.
Responsibilities
- Design and implement maintainable OS services, frameworks, and clear interfaces for inference execution, model loading and lifecycle, and resource management.
- Partner with researchers on quantization, runtime integration, and memory optimization to meet memory, compute, and energy budgets while evaluating model quality and product behavior.
- Develop scheduling and resource policies that balance inference with other device activity, preserving responsiveness within latency, memory, battery, and thermal constraints.
- Develop and validate execution strategies that adapt to workload needs, available resources, and changing device conditions.
- Use tracing, profiling, and structured debugging to investigate correctness, concurrency, performance, and reliability issues across models, inference runtimes, and OS components.
- 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.
- Collaborate with research, hardware, firmware, platform, and product engineering teams to turn emerging model capabilities into maintainable systems and reliable shipped features.
Minimum Qualifications
- 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.
- Strong understanding of scheduling, memory management, and how workloads compete for shared system resources.
- Experience diagnosing complex system behavior and delivering performance or power improvements supported by repeatable measurements.
- Ability to work across disciplines, translate research and product needs into system requirements, and explain technical decisions and tradeoffs clearly.
Nice to Have
- Delivered end-to-end on-device inference in shipped products, from model adaptation and runtime integration through OS support and production debugging.
- Adapted models for deployment through quantization, compression, or related techniques, balancing quality, execution cost, and memory requirements.
- Optimized inference across CPUs, GPUs, or neural accelerators, accounting for data movement, synchronization, and execution placement.
- Proficient in Rust for systems programming.
- Take ownership of complete solutions and follow problems across model, runtime, and operating system boundaries.
- Challenge assumptions, develop new approaches when existing techniques fall short, and use experiments and measurements to guide decisions.
- Work constructively across disciplines and make careful tradeoffs among model quality, performance, power, reliability, security, and maintainability.