Edge & Physical AI SW Product Manager

Job highlights

• Develop end-to-end system software solutions and demonstration platforms for hardware and software developers at industry events and conferences. Job Qualifications: . 8+ years of AI product management experience in software,developer tools,or AI / ML infrastructure,with direct ownership of a software product roadmap and developer-facing API surface

Job Responsibilities:

• Define and maintain the NPU software product roadmap spanning the full stack: AI/ML compiler pipeline, AI/ML tools for optimization (conversion, quantization, pruning), runtime and execution engine, SDK and developer APIs, model optimization, workflow and repository, and existing eIQ platform integration.
• Own and create a scalable platform/toolkit to support newer model architectures (CNNs, LLMs, ViTs, VLMs) and enable a path to Day 0 model support in the long run.
• Maintain familiarity with supported architectures and operators, align customer expectations with available model support, and work with Engineering to close identified gaps.
• Engage closely with GTM to help prioritize industry verticals, segments, and use cases to create an enablement strategy for all business units.
• Enable system-level solutions through pre-sales and sales strategy and support.
• Define an integrated compiler strategy and transition all NXP AI products to a unified AI workflow across business units.
• Continuously monitor AI market developments and trends (compilers, operators, frameworks, models, microservices, skills, and agents) and build or refine NXP s AI strategy.
• Drive the eIQ integration strategy-integrate the current- and next-generation Neutron compiler and SDK within NXPs eIQ developer environment and define NPU support for the existing MPU/MCU developer base. Create a cradle-to-grave plan for features and components during eIQ integration.
• Develop use-case-driven software enablement and help prioritize, reset, and reconcile existing features to deliver a holistic user experience.
• Own the upstream open-source compiler integration strategy-define NXPs participation and contribution roadmap for Apache TVM, MLIR/LLVM, ONNX Runtime, and PyTorch/TensorFlow export paths.
• Define and maintain the model zoo or an equivalent-curate, compile, quantize, and benchmark frontier models (LLaMA variants, YOLO generations, Whisper, Phi, Gemma, custom vision transformers) on Ara NPU and publish validated results.
• Own the developer experience end-to-end: SDK installation, model import workflow, compilation, profiling, debugging, and deployment-define and track developer adoption funnel metrics.
• Manage framework and ISV partner relationships-PyTorch, TensorFlow, Hugging Face, ONNX Runtime, and key vertical AI software partners; drive NPU support contributions and co-marketing. Support external software integrations and licensing partnerships.
• Run beta and early-access programs with strategic developer customers; translate feedback into prioritized engineering requirements.
• Define software versioning policy, backward compatibility commitments, and long-term support (LTS) release strategy.
• Own competitive benchmarking of model coverage against Qualcomm AI Hub, DeepX, Axelera Metis SDK, and Arm NN/ExecuTorch while preserving software ease of use.
• Maintain a working knowledge of firmware and PCIe and USB drivers.
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• Develop end-to-end system software solutions and demonstration platforms for hardware and software developers at industry events and conferences.

Job Qualifications:

• 8+ years of AI product management experience in software, developer tools, or AI/ML infrastructure, with direct ownership of a software product roadmap and developer-facing API surface.
• Strong understanding of the inference model deployment pipeline-training frameworks (PyTorch, TensorFlow), model exchange formats (ONNX, TFLite), and inference runtime environments.
• Familiarity with compiler toolchain concepts: graph optimization, operator fusion, quantization, and code generation-sufficient to write actionable requirements and evaluate engineering tradeoffs.
• Experience defining and tracking developer adoption metrics-SDK downloads, model compilations, active deployments, and support ticket analysis.
• Track record of driving open-source or partner ecosystem strategy-upstream contributions, ISV enablement, or developer community programs.
• Strong prioritization discipline-experience managing a large backlog across multiple eng…