Machine Learning Engineer

## Machine Learning Engineer
** Phoenix,AZ85083
*
* Employment Type:

Perm Job Number: 25827

Remote Options:
Hybrid##

Job Description
** Sr. Machine Learning Engineer*
* * Salary Range: $130k to $150k
* Our client is seeking a Sr. Machine Learning Engineering for a direct hire role to sit in North Phoenix, AZ or Hillsboro, OR. This role will be onsite 4 days a week and 1 remote day.

** 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
*** 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.
** GPU Acceleration
*** 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.
** Model Deployment & Optimization
*** 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.
** Concurrency & Systems Optimization
*** 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.
** Cross-Functional Collaboration
*** Partner with machine learning scientists, computer vision engineers, hardware engineers, and software developers to deliver integrated AI solutions.