Principal ML Infrastructure Engineer (Relocation Available)

Franklin Fitch; Dallas (TX); On-site; USD 100,000 - 140,000; Full time

Job summary

A technology solutions provider in Dallas is seeking an AI Infrastructure Engineer with expertise in C++ and CUDA. The role involves designing and optimizing GPU-accelerated systems for deploying machine learning models in production. Ideal candidates will have a Master's or PhD, along with strong experience in GPU optimization and high-performance system design. Responsibilities include building inference pipelines, supporting model deployment, and working closely with ML researchers to ensure models are production-ready.

Qualifications

• Strong C++ expertise with experience in production-grade systems.
• Hands-on experience with CUDA programming and GPU optimization.
• Solid understanding of GPU architectures and memory management.

Responsibilities

• Design and maintain GPU-accelerated infrastructure for ML models.
• Build and optimize inference pipelines for low-latency performance.
• Support model conversion and deployment using inference runtimes.
• Partner with ML researchers to transition models to production.

Skills

C++ expertise

CUDA programming

GPU optimization

Linux

Education

Masters or PhD

Tools

TensorRT

Job description

Overview

AI Infrastructure Engineer (GPU Systems & Model Deployment) (Principal and Entry level available)

We are seeking an AI Infrastructure Engineer to design and optimize high-performance systems that enable machine learning models to run reliably and efficiently in production environments. This role is focused on GPU-accelerated inference, low-latency model serving, and bridging the gap between research models and real-world deployment. You will work closely with ML researchers and software engineers to ensure models are production-ready, scalable, and performant.

This is a hands-on systems role with a strong emphasis on C++, CUDA, and GPU inference optimisation.

Core Responsibilities

• Design and maintain GPU-accelerated infrastructure for deploying machine learning models in production
• Build and optimize high-throughput, low-latency inference pipelines
• Develop and maintain performance-critical components in C++
• Optimize GPU utilization through CUDA programming and kernel tuning
• Support model conversion, optimization, and deployment using inference runtimes
• Partner with ML researchers to transition models from experimentation to production
• Diagnose and improve system performance relative to baseline benchmarks
• Ensure deployed systems are reliable, observable, and maintainable in production environments

Required Qualifications

• Masters or PhD required
• Strong C++ expertise with experience writing and optimizing production-grade systems
• Hands-on CUDA programming experience and GPU performance optimization
• Solid understanding of GPU architectures and memory management

Preferred / Nice-to-Have Qualifications

• Experience with TensorRT or similar GPU inference runtimes
• 1–7 years of experience as a Software Development Engineer supporting production model deployment
• Experience with model optimization, quantization, or runtime acceleration techniques
• Exposure to ML frameworks (e.g., PyTorch, TensorFlow) from a systems or deployment perspective
• Experience working with containerized environments and CI/CD pipelines

Tech Environment (Representative, Not Exhaustive)

• C++, CUDA
• GPU inference runtimes (e.g., TensorRT)
• Linux, containers, cloud or on-prem GPU systems