Embedded Software Engineer

RealSense delivers industry-leading depth cameras and vision technology used in autonomous mobile robots, humanoids, access control, industrial automation, healthcare and more. With a mission to deliver world class perception systems for Physical AI and safely integrate robotics and AI into everyday life, RealSense provides

intelligent, secure and reliable vision systems that help machines navigate and interact with the human world.

About the Role

Facial Authentication is one of RealSense's flagship products - a secure biometric solution used in access control and identity verification around the world. It's a category of its own: high-accuracy, anti-spoofing face recognition designed for real-world security.

We're looking for a Software Engineer to take a key role in developing and maintaining this product. You'll work on a complex pipeline that combines deep learning inference, embedded systems, and real-time device–host communication.

As part of a fast-paced startup environment, you'll be expected to take ownership, think system-wide, and contribute hands-on to a high-impact product used in real-world applications.

What you’ll do

• Implement and maintain software for facial biometrics and computer vision applications
• Develop in C++ and C on Linux-based embedded platforms
• Work closely with algorithm and hardware teams to integrate deep learning models into a robust pipeline
• Debug and optimize complex systems involving real-time data processing and device-host communication
• Own features end-to-end from design to deployment
• Contribute to architectural decisions and code reviews in a highly collaborative, engineering-driven culture

Requirements

• 5+ years of hands-on experience in C++ and C development
• Strong software engineering and debugging skills
• Experience with embedded systems and performance/resource constraints
• Proven ability to work independently and own complex components
• Understanding of system-wide architecture and integration of various modules

Advantages

• Background in computer vision or deep learning inference
• Familiarity with frameworks like OpenCV, TensorFlow, or ONNX Runtime
• Experience with device–host communication protocols (e.g., USB, UART, TCP/IP)
• Exposure to startup environments or fast-moving product development cycles