Embedded AI Tooling Engineer

Analog Devices•Wilmington, MA

About The Position

Analog Devices is uniquely positioned for success at the boundary of the physical and digital worlds. Analog Devices transforms physical phenomena – sound, light, radio waves, voltages, currents, and motion – into high fidelity data. Our mission is to build the Intelligent Edge where AI transforms the way we solve challenging problems by combining deep application knowledge, close customer relationships, extraordinary data, advanced circuits, and breakthrough algorithms. Analog Devices has established the Embedded AI Tooling Team to provide development of next-generation embedded AI deployment infrastructure and model optimization tools for cutting-edge SoCs. Join our technically diverse team of AI infrastructure experts to unlock unprecedented AI performance in every domain. Build Embedded AI Deployment Infrastructure for Intelligent Edge SoCs. You will be a member of a core tooling team and will be instrumental in designing and implementing cutting-edge technology that will transform the semiconductor industry and enable embedded system engineers and research scientists to deliver AI-based solutions using ADI hardware. During the first year, you will be part of a team responsible for achieving the following milestones: Work with team members to design, implement, and release novel AI model deployment tools and infrastructure for heterogeneous computer architectures such as DSPs, NPUs, and CPUs as part of a larger SoC. Build end-to-end workflows spanning model development, optimization, hardware-specific architecture, and deployment to ADI's embedded platforms—enabling developers to seamlessly move from model conception to production on resource-constrained devices. Develop tools and infrastructure for hardware-aware model design, including architecture mapping techniques that adapt neural network structures to leverage specific hardware capabilities (DSP operations, NPU features, mixed-precision computation). Design and implement model compilation and optimization pipelines that enable seamless quantization, pruning, layer fusion, and hardware-specific code generation for ADI's embedded hardware platforms. Explore and prototype agentic AI workflows that leverage autonomous agents for automated model-hardware co-optimization, intelligent architecture search, and adaptive deployment strategies. Become a thought leader in developing a company-wide strategy around production-quality embedded AI deployment infrastructure and hardware-aware model development tooling.

Requirements

Strong embedded systems and computer architecture experience (bare-metal, RTOS, or embedded Linux).
Expertise in end-to-end AI/ML model development, from training through optimization and deployment on embedded platforms.
Experience with hardware-aware neural architecture design and model optimization techniques tailored to specific processor architectures.
Proficiency in C, C++, Python, with experience in firmware and low-level software development.
Deep understanding of neural network quantization, pruning, knowledge distillation, and optimization techniques for resource-constrained devices.
Knowledge of neural network accelerators (NPUs, DSPs) and efficient execution of neural networks on heterogeneous hardware.
Familiarity with AI/ML frameworks (TensorFlow, PyTorch) and deployment tools (TensorFlow Lite, ONNX Runtime, TVM, etc.).
Experience with build systems (CMake, Make, Ninja), CI/CD pipelines, and infrastructure automation.
Background in ML algorithms (CNN, DNN, Transformer architectures) and their embedded implementation.
Familiarity with developer tooling (debuggers, profilers, SDKs, system configuration tools).

Nice To Haves

Experience with hardware-software co-design and custom operator development for specialized hardware.
Knowledge of neural architecture search (NAS) and automated model optimization techniques.
Background in digital signal processing (DSP) and algorithm implementation experience.
Experience with agentic AI systems, multi-agent frameworks, or autonomous optimization workflows.
Experience with edge AI frameworks and deployment tools (TensorFlow Lite Micro, ONNX, Apache TVM, MLIR).
Understanding of compiler optimizations and code generation for embedded AI accelerators.
Experience with FPGA development including design, synthesis, simulation, and verification.
Experience with Zephyr RTOS and open-source RTOS ecosystems.
Experience contributing to and working with open-source ecosystems.
Understanding of heterogeneous architectures (ARM, RISC-V, DSPs, custom SoCs).

Responsibilities

Work with team members to design, implement, and release novel AI model deployment tools and infrastructure for heterogeneous computer architectures such as DSPs, NPUs, and CPUs as part of a larger SoC.
Build end-to-end workflows spanning model development, optimization, hardware-specific architecture, and deployment to ADI's embedded platforms—enabling developers to seamlessly move from model conception to production on resource-constrained devices.
Develop tools and infrastructure for hardware-aware model design, including architecture mapping techniques that adapt neural network structures to leverage specific hardware capabilities (DSP operations, NPU features, mixed-precision computation).
Design and implement model compilation and optimization pipelines that enable seamless quantization, pruning, layer fusion, and hardware-specific code generation for ADI's embedded hardware platforms.
Explore and prototype agentic AI workflows that leverage autonomous agents for automated model-hardware co-optimization, intelligent architecture search, and adaptive deployment strategies.
Become a thought leader in developing a company-wide strategy around production-quality embedded AI deployment infrastructure and hardware-aware model development tooling.