Remote ML Systems Engineer: High-Performance Inference

Bright Vision Technologies; Austin (TX); Remote; USD 145,000 - 165,000; Full time

Job summary

Bright Vision Technologies in the United States seeks an ML Systems Engineer to design, build, and operate high-performance inference platforms for serving large ML models in production. The role emphasizes distributed systems reliability, low latency, and scalable serving architectures.

You will own the systems engineering stack, optimize latency, throughput, and cost, and collaborate with data, platform, and software teams to deliver robust AI services at scale.

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.
• Proficiency in Python and a systems language (Go, Rust, or C++).
• Experience operating high-throughput, low-latency services in production.
• Hands-on with LLM inference frameworks (vLLM or TensorRT-LLM).
• Understanding of GPU architecture, memory hierarchies, and accelerator utilization.
• Familiarity with Kubernetes, autoscaling, and cloud platforms.
• Experience with observability stacks (metrics, tracing, logging).
• Strong capacity planning and performance engineering knowledge.
• Excellent communication and incident response skills.

Responsibilities

• Design and operate high-performance ML inference platforms for production workloads.
• Build scalable, reliable serving systems with attention to latency, throughput, and cost.
• Implement request routing, batching, caching, and autoscaling strategies.
• Monitor GPU utilization and ensure end-to-end observability across workloads.

Skills

Distributed systems

Python

Go/Rust/C++

Observability

Performance engineering

Incident response

Education

Bachelor’s or Master’s in Computer Science

Tools

Kubernetes

vLLM

TensorRT-LLM

Job description

Bright Vision Technologies in the United States seeks an ML Systems Engineer to design, build, and operate high-performance inference platforms for serving large ML models in production. The role emphasizes distributed systems reliability, low latency, and scalable serving architectures.

You will own the systems engineering stack, optimize latency, throughput, and cost, and collaborate with data, platform, and software teams to deliver robust AI services at scale.