System Performance & AI Architect

System Performance & AI Architect Majestic Labs Los Altos, CA Job Details Full-time 13 hours ago Qualifications Machine learning/AI-based analysis Computer Science Automation Doctoral degree in engineering Engineering Electrical engineering Performance testing Doctoral degree in Computer Science Analysis skills C++ SoC Presentation skills Doctor of Philosophy Distributed systems Mentoring Scalability System architecture design Simulation systems Quantitative analysis Senior level Electrical Engineering AI Cross-functional collaboration Semiconductor experience Doctoral degree in electrical engineering Leadership Python Cross-functional communication 10 years Full Job Description The Mission As 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.

Key Responsibilities System-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 Qualifications Education:

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 Leadership Strategic 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.