Job Description
Title: Sr. Machine Learning Engineering
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Duration: Full Time
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Location: North Phoenix, AZ or Hillsboro, OR. Onsite 4 days a week and 1 day remote
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Pay Range: $130,000-$150,000k
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JOB SUMMARY
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The role of Senior Machine Learning Engineer will architect and optimize real-time, high-throughput, and ultra-low latency image pipelines for next-generation Mask Inspection Tools. Responsibilities include eliminating hardware bottlenecks through CUDA kernel tuning and GPU parallel computing, ensuring deep learning models and CV algorithms seamlessly processing massive, high-bandwidth streaming data at production scale.
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ESSENTIAL DUTIES AND RESPONSIBILITIES
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High-Performance Computing Pipeline Architecture
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- Design, implement, and optimize high-throughput, low-latency image processing pipelines for real-time optical inspection and machine vision systems.
- Develop scalable architectures capable of processing large volumes of imaging data while meeting stringent latency and reliability requirements.
- Profile and optimize system performance across CPU, GPU, memory, and I/O subsystems
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GPU Acceleration
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- Design, develop, and optimize CUDA kernels to accelerate deep learning inference and classical computer vision algorithms.
- Maximize GPU utilization through efficient memory management, kernel optimization, and parallel programming techniques.
- Evaluate and implement performance improvements using NVIDIA GPU technologies and profiling tools.
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Model Deployment & Optimization
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- Optimize, quantize, and deploy machine learning models using TensorRT, ONNX Runtime, or similar inference frameworks.
- Integrate AI models into production-grade C++ and Python applications.
- Improve inference throughput, latency, and resource utilization while maintaining model accuracy.
- Develop automated deployment and validation pipelines for machine learning models.
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Concurrency & Systems Optimization
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- Architect and implement multi-threaded, high-concurrency software components for data acquisition, buffering, streaming, and real-time processing.
- Design robust synchronization and communication mechanisms between hardware interfaces and AI processing pipelines.
- Optimize end-to-end system performance for deterministic, real-time execution.
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Cross-Functional Collaboration
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- Partner with machine learning scientists, computer vision engineers, hardware engineers, and software developers to deliver integrated AI solutions.
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