Job Description

Locations

• Seattle, Washington, United States 98117

Salary

Skills Required

• Embedded Machine Learningintermediate
• Deploying ML modelsintermediate
• Optimizing ML algorithmsintermediate
• Power-efficient codingintermediate
• Custom silicon programmingintermediate
• Microcontroller programmingintermediate
• Handling memory constraintsintermediate
• Managing computational budgetsintermediate
• Real-time performance optimizationintermediate
• Perception Algorithm developmentintermediate
• AI and embedded hardware integrationintermediate

Required Qualifications

• Bachelor’s degree (3+ years experience) or Master’s degree (1+ year experience) in CS, EE, or a related technical field. (experience, 3 years)
• Proficiency in C/C++ for embedded systems development, including RTOS, microcontrollers, and low-level hardware interactions. (experience)
• roven ability to optimize and deploy ML models for resource-constrained edge devices using techniques like - quantization/pruning and frameworks (e.g., TensorFlow Lite, ONNX Runtime, Core ML). (experience)
• Strong analytical and debugging skills to resolve performance bottlenecks across hardware, firmware, and ML inference. (experience)

Preferred Qualifications

• Experience with ML inference hardware acceleration (DSPs, NPUs, ASICs).Familiarity with diverse neural network architectures and training methodologies for efficient edge deployment. (experience)
• Knowledge of computer vision, NLP, or audio processing in an embedded/robotics context. (experience)
• Experience with embedded Linux or other RTOS in a production environment. (experience)
• Contributions to open-source embedded ML projects or relevant publications. (experience)
• Proficiency with Python for automation and data analysis. (experience)

Responsibilities

• This role offers a unique opportunity to innovate at the intersection of AI and embedded hardware. You will transform advanced ML algorithms into highly optimized, power-efficient code for custom silicon and microcontrollers in Apple products, specifically for robotics. You'll tackle complex challenges like memory constraints, computational budgets, and real-time performance, ensuring ML models deliver exceptional user experiences while adhering to Apple’s privacy and power efficiency standards.
• Design and implement efficient ML inference pipelines on resource-constrained embedded hardware.
• Optimize neural network models (e.g., quantization, pruning) for performance, memory, and power on edge devices.
• Develop and integrate robust C/C++ low-level software for deploying ML models on microcontrollers, DSPs, and ML accelerators.
• Analyze and debug performance bottlenecks and power consumption across the hardware/software stack for ML workloads.
• Collaborate with ML researchers, hardware engineers, and platform teams to deliver high-quality, power-efficient edge AI solutions.
• Evaluate and recommend embedded platforms, toolchains, and ML frameworks for on-device intelligence applications.

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