Job Description

Summary

We are seeking an experienced Signal/Image Processing and Machine Learning engineer to join our team. The role focuses on developing innovative, real-time deep learning and signal processing solutions for new sensing and imaging systems, including video restoration. You will be part of an interdisciplinary team developing technologies that will shape future Apple products.

Description

Algorithm and model development:

• Design, implement, and optimize signal processing and machine learning models for sensing and imaging tasks, including video restoration.
• Develop and validate models using real sensor data, simulated data, and public datasets.
• Prepare, filter, process, and analyze large-scale datasets to train and evaluate models.
• Analyze your algorithm's results and benchmark them against leading methods, and theoretical bounds.
• Optimize models for real-time performance, low latency, and resource efficiency on-device.

Sensing and research:

• Develop algorithms that combine classical signal processing with learned models to extract robust information from sensor data.
• Investigate efficient architecture patterns (lightweight backbones, attention mechanisms, recurrent designs) suitable for real-time, on-device inference.
• Explore how algorithmic approaches can mitigate artifacts and limitations of new sensing and imaging systems.
• Stay current with recent advances in signal processing, computer vision, and machine learning.

Collaboration and technical leadership:

• Work closely with ML researchers, SW, HW, sensor, and architecture engineers to translate research ideas into robust models and practical, shipping systems.
• Provide technical guidance and mentorship to junior engineers, and help shape the team's technical direction.

Responsibilities

• Own the end-to-end development of real-time algorithms, from concept and prototyping through validation and deployment.
• Define evaluation methodology, metrics, and failure analysis processes for sensing and imaging algorithms.
• Drive trade-off decisions among accuracy, latency, power, and memory across algorithm, HW, and system constraints.

Preferred Qualifications

Experience deploying models on GPU, dedicated HW, or neural engines, including quantization, pruning, and other model compression techniques.

Experience with multi-sensor fusion, sensor calibration, or temporal/streaming signal processing.

Experience with generative priors (diffusion, flow matching) is an advantage.

Background in signal processing, physics, optics, lasers is a plus.

Minimum Qualifications

Strong foundation in signal processing, image processing, computer vision, machine learning, and deep learning.

Hands-on experience with sensing technologies (e.g., camera/image sensors, depth, radar, LiDAR, IMU, or other sensor modalities), including an understanding of sensor noise models and characteristics.

Demonstrated experience designing and deploying real-time deep learning and signal processing solutions using large-scale datasets.

Proficiency in Python and deep learning frameworks (e.g., PyTorch); experience with C++ or other performance-oriented languages is a plus.

Experience with model evaluation, debugging, experimental analysis, and failure analysis.

PhD or Master's degree in Electrical Engineering, Computer Science, or a related research field.

Proficient in written and verbal communication.

Ability to work both autonomously and collaboratively.

8+ years of relevant experience