AI​/ML Software Engineer

Position: AI/ML Staff Software Engineer
## AI/ML Staff Software Engineer Apply locations:
Richardson:
USA - California - Santa Clara:
Austin time type:
Full time posted on:
Posted Todayjob requisition :
JR-2602645
** About
* *** Global Foundries
* * Global Foundries is a leading full-service semiconductor foundry providing a unique combination of design, development, and fabrication services to some of the world’s most inspired technology companies. With a global manufacturing footprint spanning three continents, Global Foundries makes possible the technologies and systems that transform industries and give customers the power to shape their markets. For more information, visit
** About the Role
** We're looking for a seasoned AI/ML Staff Software Engineer to lead workload-driven architecture strategy across hardware and software boundaries. You will define how we study, model, and optimize AI/ML workloads for current and next-generation products, drive alignment across HW and SW engineering organizations, and serve as a technical authority on performance and architecture tradeoffs. This is a senior individual contributor role with significant cross-functional scope and organizational influence.
** Summary of Role
** We're looking for a seasoned AI/ML Staff Software Engineer to lead workload-driven architecture strategy across hardware and software boundaries. You will define how we study, model, and optimize AI/ML workloads for current and next-generation products, drive alignment across HW and SW engineering organizations, and serve as a technical authority on performance and architecture tradeoffs. This is a senior individual contributor role with significant cross-functional scope and organizational influence.
** Essential Responsibilities
** Own workload characterization and hardware performance analysis for AI/ML systems — selecting representative workloads, defining measurement methodology, building support for MIPS products (e.g., the S8200), and projecting system-level KPIs. Your findings will directly inform SoC architecture decisions, memory subsystem design, and HW/SW co-optimization strategy.

Define the software frameworks across the product portfolio: what metrics matter, how to measure them accurately, how to estimate them pre-silicon, and how to use them to make architectural bets. Leverage open-source infrastructure like MLIR and IREE to implement and validate this work. Set the standard for how the team approaches this and mentor junior engineers in applying it.

Represent software in architectural discussions with hardware teams (CPU, SoC, memory, interconnect) and software teams (compilers, runtimes, ML frameworks). Identify critical bottlenecks — compute throughput, DRAM bandwidth, on-chip memory, data movement latency, or software overhead — and build the case for specific architectural changes or optimization investments.

Present findings and recommendations to senior engineering leadership and product stakeholders. You should be as comfortable writing a one-page architectural recommendation as a detailed technical memo.
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* Other Responsibilities:

*** Perform all activities in a safe and responsible manner and support all Environmental, Health, Safety & Security requirements and programs.
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* Required Qualifications:

** BS or MS (preferred) in EE, CE, CS, or equivalent, with 5+ years in systems engineering, hardware architecture, ML systems, or performance engineering, and a track record of technical leadership.

Deep expertise in CPU and SoC architecture — memory hierarchies, out-of-order execution, vector/SIMD pipelines, power management — and how these interact with AI/ML workloads. Strong command of system-level memory bandwidth constraints (DDR/LPDDR bandwidth, channel configuration, utilization efficiency) and the ability to reason quantitatively about memory-bound vs. compute-bound workloads.

Experience with AI/ML acceleration on edge devices — NPUs, dedicated inference accelerators, DSP-based pipelines — and the HW/SW co-design challenges involved. Familiarity with model quantization, sparsity, or other efficiency techniques and their hardware interaction is a strong plus.

Familiarity with AI compiler infrastructure:…