Remote ML Infrastructure Engineer for Scalable AI Serving

Bright Vision Technologies; United States; À distance; USD 105 000 - 143 000; Plein temps

Résumé du poste

Bright Vision Technologies is seeking a Machine Learning Infrastructure Engineer to design, build, and operate high-performance inference platforms for serving large ML models in production. You will focus on systems engineering aspects of AI deployment, including routing, batching, caching, autoscaling, GPU utilization, and observability across diverse workloads.

The ideal candidate has strong distributed systems expertise and has shipped serving systems at scale, with a solid grasp of latency,

Qualifications

• Bachelor's or Master's degree in CS or related field.
• 6+ years of distributed systems, infrastructure or ML platform engineering.
• Strong Python and a systems language (Go, Rust or C++).
• Hands-on experience with high-throughput, low-latency services in production.
• Experience with LLM/inference frameworks such as vLLM or TensorRT-LLM.
• Understanding of GPU architectures and accelerator utilization.
• Familiarity with Kubernetes, autoscaling and cloud platforms.
• Experience with observability stacks and capacity planning.
• Strong communication and incident response skills.

Responsabilités

• Design and operate high-performance ML inference platforms for production.
• Focus on request routing, batching, caching, autoscaling, and GPU utilization.
• Ensure end-to-end observability across diverse model workloads.
• Balance latency, throughput, cost, and quality trade-offs in serving systems.

Connaissances

Python

Go

Rust

C++

Distributed systems

Observability

Capacity planning

Incident response

Communication

Formation

Bachelor's or Master's in Computer Science

Outils

Kubernetes

TensorRT-LLM

vLLM

Cloud platforms

Autoscaling

Description du poste

Bright Vision Technologies is seeking a Machine Learning Infrastructure Engineer to design, build, and operate high-performance inference platforms for serving large ML models in production. You will focus on systems engineering aspects of AI deployment, including routing, batching, caching, autoscaling, GPU utilization, and observability across diverse workloads.

The ideal candidate has strong distributed systems expertise and has shipped serving systems at scale, with a solid grasp of latency,