СШАSenior Staff AI Accelerator Performance Architect
4 нед. назад
СШАLeadOnsite
hardware architectureperformance analysiskernel executionsystem-level behaviorcompiler
Lead performance architecture for next-generation AI accelerator systems to optimize hardware and software integration and improve system performance.
Обязанности
- Own and evolve performance models and modeling methodologies for next-generation accelerator and system architectures.
- Build and extend analytical, simulation-based or trace-driven models across workloads, architectural features and product generations.
- Analyze important AI workloads, from individual kernels through end-to-end inference and training execution, to determine where time, bandwidth, compute and capacity are spent.
- Identify hardware and software bottlenecks and quantify opportunities to improve latency, throughput, utilization and energy efficiency.
- Evaluate proposed architectural features and determine their expected performance return across representative workloads.
- Study how models and kernels map onto the underlying compute, memory and communication architecture.
- Partner with architecture, compiler, kernel, runtime and systems teams to evaluate alternative mappings and optimizations.
- Develop workload projections and competitive performance analyses grounded in transparent assumptions.
- Create concise recommendations that translate complex performance results into architectural and product decisions.
- Improve modeling methodology, validation and correlation with RTL, emulation and silicon measurements.
- Help define representative workloads, performance targets and success criteria for future products.
Требования
- 7+ years of experience in performance analysis, performance modeling or architecture exploration for CPUs, GPUs, AI accelerators or other high-performance computing systems.
- Strong understanding of hardware architecture developed through hardware, compiler, kernel, runtime or system-performance work.
- Experience developing analytical, simulation-based or trace-driven performance models using Python, C++ or similar environments.
- Solid understanding of processor architecture, memory systems, interconnects, parallel execution and hardware resource constraints.
- Ability to move between kernel-level behavior and end-to-end application or system performance.
- Experience profiling workloads, forming performance hypotheses and validating them with quantitative evidence.
- Understanding of how software mapping and programmability affect realized hardware performance.
- Ability to communicate modeling assumptions, uncertainty, bottlenecks and recommendations clearly.
- MS or PhD in Electrical Engineering, Computer Engineering, Computer Science or equivalent practical experience.
Условия
- The base salary range for this position is $175,000 to $275,000 annually. Actual compensation may include bonus and equity, and will be determined based on factors such as experience, skills, and qualifications.
- People who are serious about software make their own hardware. At , we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined :
- Build a breakthrough AI platform beyond the constraints of the GPU.
- Publish and open source their cutting-edge AI research.
- Work on one of the fastest AI supercomputers in the world.
- Enjoy job stability with startup vitality.
- Our simple, non-corporate work culture that respects individual beliefs.
- Find out more about what it's like to work at here !
- Apply today and become part of the forefront of groundbreaking advancements in AI!
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Другое
- Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation. works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with , to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.
- Wafer-scale computing creates a distinctive architecture space in which compute placement, memory capacity and bandwidth, communication, kernel execution and system-level behavior must be understood together.
- We are looking for a performance architect to guide the evolution of our next-generation AI systems. You will connect real workloads to architectural behavior, identify the bottlenecks that matter, quantify potential improvements and influence hardware and software roadmaps through rigorous performance analysis.
- This role is ideal for someone with deep knowledge of hardware architecture, developed through hardware, compiler, kernel or system-performance work, who enjoys operating at the intersection of applications, kernels, architecture and system performance.
- Performance analysis of transformer inference or training workloads.
- Attention, GEMM/GEMV, collective communication, mixture-of-experts, quantization or memory-capacity-constrained execution.
- Kernel optimization, compiler performance, runtime scheduling or distributed accelerator systems.
- Model validation using RTL simulation, emulation, FPGA prototypes or silicon measurements.
- Competitive analysis of AI accelerators and large-scale AI systems.
- This is a performance and architecture role, not a production RTL-design position. You should be comfortable reasoning about microarchitecture and working with architecture, RTL and physical-design teams, but you will not be expected to own detailed microarchitecture specifications, production RTL implementation, synthesis closure or physical design.
- This role evaluates architectural features and recommends improvements; the AI Accelerator Architect owns the detailed feature definition and implementation-ready microarchitecture specification.
- Your primary deliverables are trusted models, workload insights, feature ROI and architectural recommendations.