Job description

Duration: 12 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. Must-Have Hard Skills: • 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 Nice-to-Have Skills: • 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. About US Tech Solutions: US Tech Solutions is a global staff augmentation firm providing a wide range of talent on-demand and total workforce solutions. To know more about US Tech Solutions, please visit www.ustechsolutions.com . US Tech Solutions is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. AI Statement: By applying, you acknowledge that AI-assisted tools may be used during hiring. #LI-AS140

Requirements

Must-haves

Duration: 12 Months
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.
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
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.
AI Statement: By applying, you acknowledge that AI-assisted tools may be used during hiring.

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Keywords to mirror

machine learningmlaipython

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Effort: medium

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