Sr. Machine Learning Engineer

Job Description

Title: Sr. Machine Learning Engineering

Duration: Full Time

Locatio n: North Phoenix, AZ or Hillsboro, OR. Onsite 4 days a week and 1 day remote

Pay Rang e: $130,000-$150,000k

JOB SUMMARY

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.

ESSENTIAL DUTIES AND RESPONSIBILITIES

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.
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• Develop scalable architectures capable of processing large volumes of imaging data while meeting stringent latency and reliability requirements.
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• 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.
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• Maximize GPU utilization through efficient memory management, kernel optimization, and parallel programming techniques.
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• 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.
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• Integrate AI models into production-grade C++ and Python applications.
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• Improve inference throughput, latency, and resource utilization while maintaining model accuracy.
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• 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.
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• Design robust synchronization and communication mechanisms between hardware interfaces and AI processing pipelines.
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• 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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