Remote ML Systems Engineer - High-Performance Inference

Bright Vision Technologies; Frisco (TX); Remote; 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. This role emphasizes the systems engineering side of AI deployment, including routing, batching, caching, autoscaling, GPU utilization, and end-to-end observability across diverse model workloads.

The ideal candidate brings strong distributed systems and performance engineering expertise, has shipped serving

Qualifications

• Bachelor’s or Master’s degree in CS or related field.
• 6+ years in distributed systems, infrastructure, or ML platform engineering.
• Strong Python and a systems language (Go, Rust, or C++).
• Experience running high-throughput, low-latency services in production.
• Experience with LLM inference frameworks such as vLLM or TensorRT-LLM.
• Familiar with Kubernetes, autoscaling, and cloud platforms.
• Experience with observability stacks (metrics, tracing, logging).
• Strong capacity planning and performance engineering.

Responsibilities

• Design, build, and operate high-performance inference platforms for ML models.
• Optimize routing, batching, caching, and autoscaling for ML serving.
• Monitor reliability and observability across diverse workloads.
• Collaborate with teams on deployment, cost, and latency trade-offs.

Skills

Python

Go

Rust

C++

Distributed systems

Performance engineering

Education

Bachelor’s or Master’s in Computer Science or related field

Tools

Kubernetes

vLLM

TensorRT-LLM

Cloud platforms

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. This role emphasizes the systems engineering side of AI deployment, including routing, batching, caching, autoscaling, GPU utilization, and end-to-end observability across diverse model workloads.

The ideal candidate brings strong distributed systems and performance engineering expertise, has shipped serving