Embedded Computer Vision & Machine Learning Engineer

Job Title: Embedded Computer Vision & Machine Learning Engineer

Location: Sunnyvale, CA (Hybrid, 3 days onsite)

Duration: 6-Month Contract (Potential for Extension)

We are seeking an experienced Embedded Computer Vision & Machine Learning Engineer to develop and optimize advanced AI solutions for edge devices. In this role, you will work at the intersection of machine learning, computer vision, embedded systems, and imaging technologies to deliver high-performance, low-latency AI experiences on resource-constrained hardware.

This is an excellent opportunity for engineers who enjoy solving challenging problems related to real-time computer vision, AI model optimization, embedded software, and camera-based systems.

Top 3 Skills

Embedded C/C++ Development

Edge AI & Computer Vision

Machine Learning Deployment & Optimization

Required Qualifications

Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field (or equivalent practical experience).

Strong programming experience with C, C++, and Python.

Experience deploying machine learning models to edge or embedded devices.

Hands-on experience with frameworks such as TensorFlow Lite, ONNX Runtime, PyTorch Mobile, or similar technologies.

Knowledge of computer vision, image processing, or machine learning algorithms.

Understanding of embedded systems and hardware-constrained software development.

Experience optimizing software for performance, memory, and power efficiency.

Familiarity with AI inference acceleration on edge hardware.

Preferred Qualifications

Experience working with camera systems, image sensors, or multimedia processing pipelines.

Knowledge of model optimization techniques including quantization, pruning, and runtime acceleration.

Experience developing real-time vision or AI applications on embedded platforms.

Familiarity with GPU, DSP, or NPU-based acceleration technologies.

Experience debugging and profiling low-level software and hardware interactions.

Background in robotics, AR/VR, imaging, mobile devices, or edge AI products.

Responsibilities

Design, develop, and deploy computer vision and machine learning solutions on embedded and edge hardware platforms.

Optimize AI workloads for performance, latency, power efficiency, and memory utilization.

Develop software in C++, C, and Python for real-time image processing and machine learning applications.

Integrate machine learning models into camera, imaging, and multimedia processing pipelines.

Collaborate with software, hardware, and systems engineering teams to deliver production-quality solutions.

Analyze system performance and troubleshoot bottlenecks across software and hardware components.

Optimize AI models for deployment using hardware accelerators such as NPUs, GPUs, and DSPs.

Support development of low-latency vision applications including image enhancement, object detection, tracking, and related real-time AI features.