Embedded Signal Processing and ML Algorithms Engineer

Role Number: 200692002-3543

Summary

Join Apple's Human Interface Design (HID) team to develop advanced sensing, tracking, and classification algorithms to enable best-in-class user experiences across Mac, iPhone, iPad, Apple Pencil, Apple Watch, and exciting new products. We are looking for a strong machine learning & DSP engineer with a passion for bridging algorithm development with embedded system design and software integration and leveraging GenAI tools into an engineering workflow. Our team brings togethers experts of diverse backgrounds: Machine Learning, Signal Processing, Firmware, Software, Physics, Mathematics, Human Factors, and more. You will work at the intersection of hardware, software, and design, collaborating with specialists across Apple to ship extraordinary products used every day by millions of people around the world.

Description

This is an machine learning engineering role at the HW/SW/UI boundary, with broad collaboration opportunities across firmware, software, and algorithms teams. You will be responsible for developing sensing algorithms using signal processing and machine learning techniques and delivering them as highly optimized, power-efficient code for custom silicon. You'll tackle complex challenges like memory constraints, computational budgets, and real-time performance while ensuring robust end-to-end functionality from sensor data acquisition through to the software layer to deliver exceptional user experiences. In this role, you will also lead efforts to identify, evaluate, and deploy GenAI tools that improve team productivity, including GenAI-assisted development, data analysis, and workflow automation. You will help define best practices for GenAI tool adoption within the group and collaborate cross-functionally to scale these efforts.

Minimum Qualifications

• Bachelor’s degree in Electrical Engineering, Computer Engineering, Computer Science, or related technical field and 3 years of relevant industry experience

• Strong software programming skills in C/C++, Python, and experience with embedded systems

• Strong background in signal processing, machine learning and statistical learning algorithms

• Experience with object-oriented programming and common software design patterns

• Experience leveraging AI-powered development tools to streamline daily workflows, accelerate coding and debugging, creating test frameworks and improve overall code quality and testing

• Excellent communication, presentation, and documentation skills

Preferred Qualifications

• Master’s/PhD degree with 3+ years of experience in algorithm integration or embedded/software systems

• Proven track-record in signal processing and machine learning, including areas such as time-series analysis, digital filtering, feature extraction, classification, regression, or neural network-based inference; experience applying these techniques to sensor data or embedded sensing systems is a strong plus

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

• Understanding of modern SoC/ASIC architectures, hardware neural/math accelerators, memory-bandwidth bottlenecks and experience with HW/SW partitioning for complex algorithms is a strong plus

• Experience working at the HW/SW interface, including familiarity with firmware interfaces, driver layers, or OS-level integration

• Hands-on experience prototyping, developing and integrating algorithm and signal processing code with software layer (e.g., OS-level frameworks, or application software)

• Track record of evaluating, integrating and deploying AI-powered developer tools (e.g., LLM-assisted coding, AI-based test generation, intelligent data analysis pipelines)

• Strong cross-functional collaboration skills with the ability to work effectively across firmware, software, and algorithms disciplines

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics.