Camera ISP Computer Vision & Machine Learning Algorithm Engineer
Apple · Cupertino ·
- Category
- ML ai
- Company size
- 1000+
Apple · Cupertino ·
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Engineer on Apple's Camera Algorithm team developing embedded, lightweight computer vision and machine learning algorithms for image signal processing across Apple product cameras — prototyping, optimizing for power/performance, and productizing them on Apple ISP hardware and the Apple Neural Engine. Core stack includes C/C++, Python, MATLAB, traditional CV, and ML.
At Apple, the Camera Algorithm team is looking for extraordinary algorithm engineers to work on the image capture and processing/rendering algorithms that serve all Apple product cameras! As part of the team, you will work on core camera/ISP/machine learning technologies based on Apple-crafted image signal processing pipelines and hardware components, such as the Apple Neural Engine. Here you will have the chance to define the way Apple cameras capture, process, and render outstanding still image and video quality. Our close-knit team champions an environment of product innovation, rapid product iteration, and collaboration at both team and cross-functional levels, granting you a liberating amount of autonomy. We work closely with various teams, such as Silicon Design, Camera HW/SW, and QA. Our team environment is dynamic, fast-paced, and requires a self-starter attitude.
In this role, you will be responsible for prototyping, implementing, and productizing embedded, lightweight computer vision algorithms for ISP processing and capture control.
The ideal candidate should be able to balance optimization and performance in resource-constrained environments, keep up with rapid technological advancements in both computer vision and hardware, and ensure seamless integration with existing systems and processes.
-Develop lightweight computer vision algorithms suitable for embedded systems, focusing on efficiency and performance. ---Utilize both traditional CV techniques (e.g., image processing, feature detection, IMU) and modern learning-based approaches. -Drive the development of algorithms from concept to deployment. Optimize algorithms for power and processing efficiency, and ensure they meet the necessary performance benchmarks.
-Use camera simulations to prototype and test algorithms before hardware implementation. -Conduct on-device testing and field testing to validate algorithm performance in real-world scenarios.
-Work closely with various teams, such as SOC architecture, firmware, and other camera technology units, to integrate algorithms effectively. -Ensure smooth deployment and operation of algorithms within the broader camera system architecture. -Collaborate with multiple teams to deliver optimized solutions.
ManoMotion · Stockholm, Stockholms län, Sverige