Remote ML Systems Engineer for Scalable Inference

Bright Vision Technologies; Pflugerville (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 ML models in production. This role focuses on the systems engineering side of AI deployment, including request routing, batching, autoscaling, and observability across diverse model workloads.

The ideal candidate has 6+ years in distributed systems or ML platform engineering, strong Python and systems language skills (Go, Rust, or C++), and hands-on

Qualifications

• Bachelor's or Master's degree in Computer Science or 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.

Skills

Python

Distributed systems

Performance engineering

Capacity planning

Kubernetes

Cloud platforms

Observability

Incident response

Education

Bachelor's or Master's degree in Computer Science or related field

Tools

vLLM

TensorRT-LLM

Go

Rust

C++

Kubernetes

Job description

Bright Vision Technologies is seeking an ML Systems Engineer to design, build, and operate high-performance inference platforms for serving large ML models in production. This role focuses on the systems engineering side of AI deployment, including request routing, batching, autoscaling, and observability across diverse model workloads.

The ideal candidate has 6+ years in distributed systems or ML platform engineering, strong Python and systems language skills (Go, Rust, or C++), and hands-on