Principal Engineer – NPU Compiler & Architecture

You will work closely with hardware architects, micro-architects, compiler engineers and AI software teams to answer a fundamental question:

How should the NPU hardware and software be co-designed to deliver the best performance, power efficiency, programmability and scalability for real-world AI workloads?

What You Will Be Responsible For

Compiler & Software Architecture for NPU

• Develop compiler and software proof-of-concepts for current and next-generation NPU architectures.
• Define software abstractions and compiler flows that efficiently expose NPU hardware capabilities to AI workloads.
• Develop and evaluate compiler concepts including graph lowering, intermediate representations, operator mapping, scheduling, tiling, fusion, memory planning and code generation.
• Translate NPU architectural concepts into executable software models and demonstrate their feasibility using representative AI workloads.
• Develop lightweight compiler/runtime infrastructure to validate new hardware features before production software implementation.
• Analyze existing compiler limitations and identify architectural changes required to improve programmability and accelerator utilization.

Hardware–Software Co-Design

• Work as an integral member of the hardware architecture team to co-design NPU hardware and software.
• Analyze how proposed hardware features can be effectively exposed through the compiler and software stack.
• Provide software-driven feedback on compute architecture, memory hierarchy, data movement, dataflow, scheduling, instruction set and accelerator programmability.
• Identify hardware features that provide meaningful benefits to real AI workloads and challenge features that add hardware complexity without sufficient software value.
• Define compiler requirements and software abstractions for new NPU capabilities.
• Participate in architecture and micro-architecture reviews and influence hardware decisions from a software and workload perspective.
• Evaluate architectural trade-offs considering performance, power, area, compiler complexity and software scalability.

AI Workload & Performance Analysis

• Analyze representative AI models and workloads to identify compute, memory, bandwidth, scheduling and data-movement bottlenecks.
• Build software-based performance models and workload prototypes to evaluate architectural concepts.
• Develop experiments to quantify the impact of proposed hardware features on model performance and accelerator utilization.
• Investigate issues such as quantization, sparsity, operator fusion, tensor layouts, tiling, data reuse, memory bandwidth and scheduling efficiency.
• Correlate software/model-level performance with architectural and micro-architectural behavior.
• Use workload analysis to guide both current-generation optimizations and next-generation NPU architecture.

Architecture Prototyping

• Rapidly prototype software solutions for architectural concepts that may be months or years away from production silicon.
• Develop functional models, compiler prototypes, simulators, emulators, reference implementations or runtime abstractions as needed to validate architectural ideas.
• Demonstrate end-to-end execution of representative AI workloads on proposed NPU architectures.
• Build proof-of-concepts that allow hardware architects to make informed architectural decisions before RTL implementation.
• Help establish software models and interfaces that can later evolve into production compiler components.

Cross-Functional Leadership

• Work closely with NPU hardware architects, micro-architects, RTL designers and the production compiler/software organization.
• Bridge the communication gap between hardware and software teams and translate requirements in both directions.
• Participate in architecture definition from early concept through implementation and silicon bring-up.
• Mentor engineers and contribute to technical direction for compiler-driven hardware/software co-design.
• Influence the roadmap of future NPU architectures through workload and software-driven insights.

Necessary Qualifications

Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering or a related field.

8+ years of experience in compiler development, systems software, computer architecture, AI accelerators, or a closely related area.

Strong understanding of compiler architecture and compiler optimization techniques.

Strong C++ programming skills and proficiency in Python.

Strong understanding of computer architecture and familiarity with accelerator architectures, memory hierarchies and data movement.

Experience analyzing AI/ML workloads and understanding performance bottlenecks.

Ability to work across hardware and software boundaries and communicate effectively with hardware architects.

Strong analytical and problem-solving skills, with the ability to rapidly prototype and evaluate architectural ideas.

Good to have

• Experience developing compilers or software stacks for NPUs, GPUs, DSPs, TPUs or other AI accelerators.
• Experience with MLIR, LLVM, TOSA, StableHLO, Torch-MLIR or similar compiler infrastructure.
• Experience with accelerator-specific scheduling, tiling, memory management or code generation.
• Understanding of NPU architecture concepts such as dataflow, tensor engines, systolic/array-based compute, local SRAMs, DMA/data movement and accelerator instruction sets.

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