System Performance & AI Architect

Description About Majestic LabsWe’re a fast-moving, US-Israeli AI startup building next-generation infrastructure for the world’s most demanding AI workloads. Our mission is to accelerate the future of intelligence and make it ubiquitously accessible by delivering full stack custom AI servers optimized for large-scale inference and training.Backed by top-tier investors and led by industry veterans, we’re scaling rapidly. We seek forward-thinking engineers and operators who thrive in a collaborative environment and excel at solving complex challenges from first principles. If you want to build infrastructure that fundamentally changes what the world can accomplish with AI, come join us!About the positionAs a System Architect, you will be responsible for the end-to-end performance simulation of our next-generation AI and Graph-computing platforms. You will perform the quantitative analysis and architectural pathfinding that defines how our systems handle the world’s most complex data-centric workloads, from Trillion-parameter LLMs and Mixture-of-Experts (MoE) to large-scale, irregular Graph Neural Networks (GNNs), and other workloads.ResponsibilitiesSystem-Level Performance Projection: develop and execute high-fidelity, system-level performance models that simulate the interaction between compute clusters, Network-on-Chip (NoC), and advanced memory hierarchies (HBM4, CXL).Empirical Profiling & Characterization: Drive deep-dive performance profiling of existing hardware architectures (GPUs, NPUs, and SoCs). Use hardware counters, trace-based analysis, and telemetry to identify real-world bottlenecks in current silicon that inform future architectural iterations.Workload-Architecture Co-Design: Profile frontier AI models and graph analytics to identify deep-system bottlenecks. Translate high-level algorithmic behaviors (e.g., KV cache growth, sparse matrix traversals) into hardware architectural requirements.Memory Subsystem Innovation: Define the strategy for managing the "Memory Wall," optimizing for bandwidth, latency, and power across complex hierarchies and disaggregated memory pools.Architectural Pathfinding: Evaluate and influence the adoption of emerging system technologies. Conduct trade-off analyses that determine the multi-year roadmap for system topology and scalability.AI-Augmented Engineering: Champion an "AI-first" approach to architecture, utilizing machine learning and automation to accelerate simulation throughput and explore massive design spaces.Cross-Functional Technical Leadership: Serve as a primary bridge between Software/Compiler teams and Hardware Implementation, ensuring architectural specifications meet real-world production constraints. Requirements Technical QualificationsEducation: PhD in Electrical Engineering, Computer Science, or a related field with a focus on Computer Architecture or High-Performance Systems.Experience: 10+ years of experience in performance modeling and system architecture, with a proven track record at major semiconductor or hyper-scale AI organizations.Expertise in Data-Centric Computing: Deep understanding of Instruction Set Architectures (ISA), Cache Coherence, and Memory Consistency.Expertise in modeling Interconnect Topologies and flow control for distributed AI training and inference.Advanced proficiency in Modern C++ and Python for building sophisticated system-level simulators.Profiling Mastery: Hands-on experience with performance analysis tools (e.g., NSight, ROCm, VTune) and developing custom trace-injection tools to correlate silicon behavior with simulation models.Workload Mastery: Demonstrated ability to characterize and optimize for irregular data-flow patterns common in GNNs, LLMs, and Recommendation Systems.Professional LeadershipStrategic Influence: Experience presenting data-driven architectural recommendations to executive leadership and strategic partners based on a blend of simulation and empirical data.Customer Engagement: Proven ability to translate customer-facing performance requirements into actionable hardware specifications.Technical Mentorship: A history of elevating the technical bar for engineering teams and championing modern, automated engineering workflows.We seek smart, curious, self-driven individuals who enjoy creative problem-solving and continuous learning. Even if you don't meet all requirements, we'd love to hear from you if you have the mindset to tackle complex challenges.Majestic Labs is an equal opportunity employer deeply committed to diversity of background and thought. If you are ready to dare greatly, learn from mistakes, engage in vigorous debates, and help create a step-function shift in human technical capability, we want to meet you!Description About Majestic LabsWe’re a fast-moving, US-Israeli AI startup building next-generation infrastructure for the world’s most demanding AI workloads. Our mission is to accelerate the future of intelligence and make it ubiquitously accessible by delivering full stack custom AI servers optimized for large-scale inference and training.Backed by top-tier investors and led by industry veterans, we’re scaling rapidly. We seek forward-thinking engineers and operators who thrive in a collaborative environment and excel at solving complex challenges from first principles. If you want to build infrastructure that fundamentally changes what the world can accomplish with AI, come join us!About the positionAs a System Architect, you will be responsible for the end-to-end performance simulation of our next-generation AI and Graph-computing platforms. You will perform the quantitative analysis and architectural pathfinding that defines how our systems handle the world’s most complex data-centric workloads, from Trillion-parameter LLMs and Mixture-of-Experts (MoE) to large-scale, irregular Graph Neural Networks (GNNs), and other workloads.ResponsibilitiesSystem-Level Performance Projection: develop and execute high-fidelity, system-level performance models that simulate the interaction between compute clusters, Network-on-Chip (NoC), and advanced memory hierarchies (HBM4, CXL).Empirical Profiling & Characterization: Drive deep-dive performance profiling of existing hardware architectures (GPUs, NPUs, and SoCs). Use hardware counters, trace-based analysis, and telemetry to identify real-world bottlenecks in current silicon that inform future architectural iterations.Workload-Architecture Co-Design: Profile frontier AI models and graph analytics to identify deep-system bottlenecks. Translate high-level algorithmic behaviors (e.g., KV cache growth, sparse matrix traversals) into hardware architectural requirements.Memory Subsystem Innovation: Define the strategy for managing the "Memory Wall," optimizing for bandwidth, latency, and power across complex hierarchies and disaggregated memory pools.Architectural Pathfinding: Evaluate and influence the adoption of emerging system technologies. Conduct trade-off analyses that determine the multi-year roadmap for system topology and scalability.AI-Augmented Engineering: Champion an "AI-first" approach to architecture, utilizing machine learning and automation to accelerate simulation throughput and explore massive design spaces.Cross-Functional Technical Leadership: Serve as a primary bridge between Software/Compiler teams and Hardware Implementation, ensuring architectural specifications meet real-world production constraints.Description About Majestic LabsWe’re a fast-moving, US-Israeli AI startup building next-generation infrastructure for the world’s most demanding AI workloads. Our mission is to accelerate the future of intelligence and make it ubiquitously accessible by delivering full stack custom AI servers optimized for large-scale inference and training.Backed by top-tier investors and led by industry veterans, we’re scaling rapidly. We seek forward-thinking engineers and operators who thrive in a collaborative environment and excel at solving complex challenges from first principles. If you want to build infrastructure that fundamentally changes what the world can accomplish with AI, come join us!About the positionAs a System Architect, you will be responsible for the end-to-end performance simulation of our next-generation AI and Graph-computing platforms. You will perform the quantitative analysis and architectural pathfinding that defines how our systems handle the world’s most complex data-centric workloads, from Trillion-parameter LLMs and Mixture-of-Experts (MoE) to large-scale, irregular Graph Neural Networks (GNNs), and other workloads.ResponsibilitiesSystem-Level Performance Projection: develop and execute high-fidelity, system-level performance models that simulate the interaction between compute clusters, Network-on-Chip (NoC), and advanced memory hierarchies (HBM4, CXL).Empirical Profiling & Characterization: Drive deep-dive performance profiling of existing hardware architectures (GPUs, NPUs, and SoCs). Use hardware counters, trace-based analysis, and telemetry to identify real-world bottlenecks in current silicon that inform future architectural iterations.Workload-Architecture Co-Design: Profile frontier AI models and graph analytics to identify deep-system bottlenecks. Translate high-level algorithmic behaviors (e.g., KV cache growth, sparse matrix traversals) into hardware architectural requirements.Memory Subsystem Innovation: Define the strategy for managing the "Memory Wall," optimizing for bandwidth, latency, and power across complex hierarchies and disaggregated memory pools.Architectural Pathfinding: Evaluate and influence the adoption of emerging system technologies. Conduct trade-off analyses that determine the multi-year roadmap for system topology and scalability.AI-Augmented Engineering: Champion an "AI-first" approach to architecture, utilizing machine learning and automation to accelerate simulation throughput and explore massive design spaces.Cross-Functional Technical Leadership: Serve as a primary bridge between Software/Compiler teams and Hardware Implementation, ensuring architectural specifications meet real-world production constraints.