Remote ML Systems Engineer — Scalable Inference & GPU Ops

Bright Vision Technologies; Euless, Bedford (TX, TX); On-site; USD 145,000 - 165,000; Full time

Job summary

Bright Vision Technologies is seeking an ML Systems Engineer to design, build, and operate high-performance inference platforms for serving large machine learning models in production.

The role emphasizes systems engineering for AI deployment: request routing, batching, caching, autoscaling, GPU utilization, and end-to-end observability across diverse workloads, focusing on latency, throughput, and cost trade-offs.

Qualifications

• Bachelor’s or Master’s degree in Computer Science or a related field.
• Six or more years of experience in distributed systems, infrastructure, or ML platform engineering.
• Strong proficiency in Python and a systems language such as Go, Rust, or C++.
• Deep experience operating high-throughput, low-latency services in production.
• Hands-on experience with LLM or large model inference frameworks such as vLLM or TensorRT-LLM.
• Strong understanding of GPU architecture, memory hierarchies, and accelerator utilization.
• Familiarity with Kubernetes, autoscaling, and modern cloud platforms.
• Experience with observability stacks including metrics, tracing, and structured logging.
• Solid grounding in performance engineering and capacity planning.
• Strong communication and incident response skills.

Responsibilities

• Design, build, and operate high-performance inference platforms for serving large ML models in production.
• Optimize latency, throughput, and cost while ensuring end-to-end observability across workloads.
• Collaborate with AI researchers and platform teams to improve serving infrastructure.

Skills

Distributed systems

Performance engineering

Communication

System design

Education

Bachelor's degree in Computer Science or related field

Master's degree (preferred)

Tools

Python

Go

Rust

C++

Kubernetes

TensorRT-LLM

vLLM

AWS/GCP/Azure

Job description

Bright Vision Technologies is seeking an ML Systems Engineer to design, build, and operate high-performance inference platforms for serving large machine learning models in production.

The role emphasizes systems engineering for AI deployment: request routing, batching, caching, autoscaling, GPU utilization, and end-to-end observability across diverse workloads, focusing on latency, throughput, and cost trade-offs.