Embedded CV/ML Engineer

sgs-job-5898

Engineering
 California
Embedded
Computer Vision
Machine Learning
Camera
Sensor
Edge AI
ONNX
Python
Android
AOSP.

Contract - 12 Months + Possibility of Extension depending on performance and budget approvals.

Location: Sunnyvale, CA – 03 Days Onsite.

Job 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.

Skills:

  • 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.
  • 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 (e.g., 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.
  • Former MAANG experience.

Education/Experience:

  • Bachelor’s degree in Computer Science, Electrical Engineering, Computer Engineering, or a related technical field, or equivalent practical experience.

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