Embedded Machine Learning Engineer, Wireless Technologies & Ecosystems - Full-time

**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.