Sr. Machine Learning Engineer

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. 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.

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.