Sr. Manager, Hardware, Machine Learning/AI

Role Overview

Core Responsibilities

• Direct machine learning enablement workflows for edge AI platforms.
• Oversee front-end compiler and model-ingestion tooling roadmaps for current and future platforms.
• Drive architecture for quantization-aware training and model optimization infrastructure at scale.
• Manage model porting, optimization, and benchmarking for hardware accelerators and NPUs.
• Lead hardware and systems initiatives addressing memory, scheduling, and data-movement constraints.
• Establish standards for evaluating model fidelity and deployment quality across execution paths.
• Align technical execution with product milestones in collaboration with executive leadership.
• Recruit, mentor, and grow an engineering team focused on model deployment, compiler infrastructure, and hardware-aware ML.
• Shape the long-term direction of the edge AI stack.

Required Qualifications

• Proven experience leading or managing teams in machine learning systems, model deployment, or AI infrastructure.
• Strong experience optimizing models for edge or accelerated inference, including quantization and performance tuning.
• Experience building or extending compiler, toolchain, or graph-transformation infrastructure for ML workloads.
• Strong understanding of hardware/software co-design for inference acceleration.
• Experience with low-power edge platforms, NPUs, custom accelerators, GPUs, or related architectures.
• Ability to reason about memory allocation, execution scheduling, bandwidth constraints, and system performance.
• Experience translating research concepts into practical tooling and production workflows.
• Exceptional cross-functional leadership and a history of technical and strategic impact.

Preferred Qualifications

• Experience with quantization-aware training, post-training quantization, compression, pruning, or sparsity optimization.
• Experience deploying vision or multimodal models.
• Familiarity with ONNX and model graph transformation pipelines.
• Experience validating numerical behavior across floating-point and integer execution paths.
• Experience supporting external customers, partner enablement, or AI platform productization.
• Background in both model-level and hardware-level optimization.
• Startup or founder-level experience building end-to-end AI platform capabilities.