Remote ML Inference Platform Engineer

Bright Vision Technologies; United States; Remote; USD 105,000 - 143,000; Full time

Job summary

Bright Vision Technologies seeks a Machine Learning Infrastructure Engineer to design, build, and operate high-performance inference platforms for production ML workloads, focusing on latency, throughput, and observability.

This remote, full-time role requires 6+ years in distributed systems, strong Python and systems-language skills, and hands-on experience with LLM inference frameworks and GPU acceleration.

Qualifications

• Bachelor's or Master's in CS or related field.
• 6+ years 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 such as vLLM or TensorRT-LLM.
• Strong knowledge of GPU architecture and accelerator usage.
• Familiarity with Kubernetes, autoscaling, and cloud platforms.
• Experience with observability stacks (metrics, tracing, logging).
• Strong performance engineering and capacity planning.
• Excellent communication and incident response skills.

Responsibilities

• Design, build, and operate high-performance inference platforms for production ML workloads.
• Focus on systems engineering for AI deployment, including routing, batching, caching, and observability.
• Address latency, throughput, cost, and quality trade-offs in ML serving.

Skills

Distributed systems

Python

Go

Rust

C++

High-throughput systems

Observability

Kubernetes

Autoscaling

Performance engineering

Communication

Education

Bachelor's or Master's in CS

Tools

vLLM

TensorRT-LLM

Job description

Bright Vision Technologies seeks a Machine Learning Infrastructure Engineer to design, build, and operate high-performance inference platforms for production ML workloads, focusing on latency, throughput, and observability.

This remote, full-time role requires 6+ years in distributed systems, strong Python and systems-language skills, and hands-on experience with LLM inference frameworks and GPU acceleration.