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SoC Infrastructure​/NoC, Power & Performance Validation Lead AI Accelerator Platform

Job in Markham, Ontario, I3P, Canada
Listing for: QUALCOMM, Inc.
Full Time position
Listed on 2026-10-25
Job specializations:
  • Engineering
    Test Engineer, Hardware Engineer, Systems Engineer
Salary/Wage Range or Industry Benchmark: 206000 - 309000 CAD Yearly CAD 206000.00 309000.00 YEAR
Job Description & How to Apply Below
Position: SoC Infrastructure/NoC, Power & Performance Validation Lead, Staff (AI Accelerator Platform)
Company  Qualcomm Technologies, Inc.
Job Area  Engineering Group, Engineering Group  ASICS Engineering
General Summary  Qualcomm’s System Validation & Emulation (SVE) team is part of the central SoC validation organization responsible for ensuring  end-to-end silicon quality across pre- and post-silicon phases . This role focuses on  content quality and system-level validation enablement , offering deep exposure to Qualcomm SoCs through  bare-metal validation content , scalable automation frameworks, and complex system debugs. Engineers in this role work across HW and SW boundaries to  develop, validate, and scale system-level test content , ensuring high-quality silicon validation across a wide range of SoC domains.

Excellent Opportunity to work on  cutting-edge SoCs across multiple product segments , including:
Premium mobile and flagship platforms
Data center
AI Accelerator platform

Role Overview  The team is currently seeking candidate specifically for SOC Infrastructure/NoC, Power and Performance Validation in pre-silicon emulation and post-silicon of industry-leading ,next generation AI Accelerator and compute platform. An ideal candidate is expected to have an excellent understanding of digital design fundamentals, on-chip interconnects, writing content to validate and analyze SOC bus architecture, performance and power.

Roles & Responsibilities   Come up with thorough performance validation plans and work closely with team members to ensure Performance-Power trade-offs are factored in
SOC Bandwidth, QoS, Latency and Throughput analysis
Minium Requirements   BS or MS or PhD in Electrical Engineering, Computer Engineering, Computer Science, or related field.
7+ years of experience in silicon validation, or platform bring-up.
Interconnect architectures - No

Cs, AXI, AHB etc. Knowledge of upstream and downstream operations and the ability to write programs to stress their transactions for appropriate coverage
Solid expertise in various FIFO designs and related performance/functionality aspects
Memory hierarchy and caches - coherency, consistency (ordering), memory types and attributes, synchronization & semaphores, full-system concurrency, MTE, MPAM
Thorough understanding of CPU and DDR concepts, Multimedia, GPU, Peripherals
System low power modes, Dynamic clock and voltage scaling operations, Interrupt architecture
Debug architecture - interactions with JTAG based debuggers, state dump scripts
In depth debugging experience for Complex HW and SW issues
Experience working with Perfmon architecture
-performance counters, telemetry data, and hardware/software co-optimization.
Drive correlation between pre-silicon models, emulation platforms, and silicon performance measurements.
Evaluate AI inference and training workloads to ensure optimal throughput, latency, memory bandwidth, and power-performance efficiency.
Contribute to methodology development for performance characterization, benchmarking, and KPI tracking across product generations.
Create performance dashboards, reports, and technical reviews for leadership and cross-functional stakeholders.
Experience in embedded systems/expertise in C, C++, Python and assembly languages. Design and Implementation of drivers and test content
Logical thinking and problem-solving ability with focus on Power, Performance and SoC System feature validation
Fundamental understanding of Static, Leakage and Dynamic power in a semiconductor design
Familiarity in debug tools including JTAG and kernel debuggers
Preferred Qualification   Strong understanding of computer architecture, AI/ML accelerators, memory hierarchy, cache coherency, and on-chip interconnects.
Knowledge of AI/ML workloads, tensor operations, matrix multiplication, transformer models, and large-scale…
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