Remote ML Systems Engineer: High-Performance Inference & Scale

Bright Vision Technologies; United States; Remote; USD 145,000 - 165,000; Full time

Job summary

Bright Vision Technologies is looking for an ML Systems Engineer to design, build, and operate high-performance inference platforms for serving large machine learning models in production. The role focuses on systems engineering, including request routing, batching, caching, autoscaling, GPU utilization, and end-to-end observability across diverse model workloads.

The ideal candidate has strong distributed systems and performance engineering experience, with a track record shipping serving

Qualifications

• Bachelor’s or Master’s degree in Computer Science or a related field.
• Six+ years of experience in distributed systems, infrastructure, or ML platform engineering.
• Strong proficiency in Python and a systems language (Go, Rust, or C++).
• Deep experience operating high-throughput, low-latency services in production.
• Hands-on experience with LLM or large model inference frameworks (vLLM or TensorRT-LLM).
• Strong understanding of GPU architecture and accelerator utilization.

Responsibilities

• Design, build, and operate high-performance inference platforms for production ML models.
• Manage request routing, batching, caching, and autoscaling for ML serving workloads.
• Optimize GPU utilization and observability across diverse model workloads.
• Ensure low latency, high throughput, and cost efficiency in serving systems.
• Collaborate on deployment pipelines and incident response for production ML infra.

Skills

Python

Go

Rust

C++

Distributed systems

Performance engineering

LLM serving frameworks

Kubernetes

Observability

Incident response

Capacity planning

Education

Bachelor’s or Master’s in Computer Science

Tools

vLLM

TensorRT-LLM

Kubernetes

Job description

Bright Vision Technologies is looking for an ML Systems Engineer to design, build, and operate high-performance inference platforms for serving large machine learning models in production. The role focuses on systems engineering, including request routing, batching, caching, autoscaling, GPU utilization, and end-to-end observability across diverse model workloads.

The ideal candidate has strong distributed systems and performance engineering experience, with a track record shipping serving