Remote ML Systems Engineer: Scalable Inference

Bright Vision Technologies; Wildwood (MO); 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 focuses on the systems engineering side of AI deployment, including request routing, batching, caching, autoscaling, GPU utilization, and end-to-end observability across model workloads.

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

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.

Skills

Distributed systems

Performance engineering

Incident response

Python

Go / Rust / C++

GPU utilization

Observability

Communication

Education

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

Tools

Kubernetes

vLLM

TensorRT-LLM

Autoscaling

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 focuses on the systems engineering side of AI deployment, including request routing, batching, caching, autoscaling, GPU utilization, and end-to-end observability across model workloads.

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