HiringCafe
Save Job
Posted 8mo ago

Embedded Machine Learning Engineer, Wireless Technologies & Ecosystems

@ Apple
View All Jobs
Seattle, Washington, United States
OnsiteFull Time
Responsibilities:deploying models, optimizing hardware, debugging performance
Requirements Summary:Design and deploy efficient on-device ML models for embedded hardware; strong C/C++, RTOS, edge ML on low-power devices; experience with quantization, pruning, TensorFlow Lite/ONNX/Core ML; solve performance across hardware, firmware, and ML.
Technical Tools Mentioned:C/C++, RTOS, Embedded Linux, TensorFlow Lite, ONNX Runtime, Core ML, Python
Save
Mark Applied
Hide Job
Report & Hide
Job Description

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.