Embedded Software Engineer II

Job Title: Embedded Software Engineer
Location: Sunnyvale, CA (Onsite days)
Duration: months

Responsibilities

• Design and deploy high-performance computer vision and machine learning pipelines directly onto resource-constrained edge hardware.
• Optimize concurrent ML workloads to maximize throughput and minimize latency when running multiple edge-AI features simultaneously.
• Integrate edge-AI algorithms seamlessly within the Android camera framework and multimedia subsystems.
• Collaborate across software and hardware teams to bridge low-level camera sensor subsystems with on-device neural processing units (NPUs).
• Profile and optimize memory footprint, power consumption, and execution timing to meet strict on-device performance constraints.

Minimum Qualifications

• Bachelor’s degree in Computer Science, Electrical Engineering, Computer Engineering, or a related technical field, or equivalent practical experience.
• Proficiency in Python, and C and C for low-level software development and optimization.
• Experience with machine learning frameworks optimized for edge deployment, such as TensorFlow Lite, ONNX Runtime, or PyTorch Edge.
• Familiarity with the Android platform stack, specifically regarding multimedia or camera subsystem integration.
• Solid understanding of computer vision fundamentals, digital signal processing (DSP), or basic camera architectures.
• Previous AI experience

Preferred Qualifications

• Hands-on experience developing within the Android camera framework to control, process, route, and transform real-time image streams.
• Experience with model optimization techniques such as quantization, pruning, and hardware-specific compilation for NPUs, GPUs, or DSPs.
• Background in developing low-latency algorithms for real-time edge applications (, biometric verification, image restoration, or on-device language processing).
• Familiarity with hardware-constrained vision pipelines, including specialized sensor integration and multi-threaded concurrency models.
• Hands-on experience debugging low-level software utilizing hardware profiling tools to isolate memory bottlenecks and scheduling conflicts.