Job Details

Weekly Hours: 40

Role Number: 200626983-3337

Summary

Join Apple's innovative iOS Robotics team within Wireless Technologies and Ecosystems (WTE). We're expanding the DockKit Framework's focus on accessories, algorithms, and user experiences to make iOS a leading platform for Perception Algorithm development. As an Embedded Machine Learning Engineer, you'll deploy efficient, low-power ML models directly onto embedded hardware, driving advanced, on-device intelligent experiences for millions of users in robotics and intelligent systems.

Description

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.

Minimum Qualifications

• Bachelor’s degree (3+ years experience) or Master’s degree (2+ year experience) in CS, EE, or a related technical field.

• Proficiency in C/C++ for embedded systems development, including RTOS, microcontrollers, and low-level hardware interactions.

• Proven 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).

• Strong analytical and debugging skills to resolve performance bottlenecks across hardware, firmware, and ML inference.

Preferred Qualifications

• Experience with ML inference hardware acceleration (DSPs, NPUs, ASICs).Familiarity with diverse neural network architectures and training methodologies for efficient edge deployment.

• Knowledge of computer vision, NLP, or audio processing in an embedded/robotics context.

• Experience with embedded Linux or other RTOS in a production environment.

• Contributions to open-source embedded ML projects or relevant publications.

• Proficiency with Python for automation and data analysis.