Senior Model Serving Engineer – Remote AI Infra

United States Digital Space LLC; United States; Remote; USD 74,000 - 98,000; Full time

Job summary

Bright Vision Technologies seeks a Model Serving Engineer to design, build, and operate high-performance inference platforms for serving large machine learning models in production. Focus is on the systems engineering side of AI deployment, including routing, batching, caching, autoscaling, GPU utilization, and end-to-end observability.

The ideal candidate brings strong distributed systems and performance engineering expertise, has shipped serving systems at scale, and understands

Qualifications

• Bachelor’s or Master’s degree in Computer Science or related field.
• Six+ years in distributed systems, infra, or ML platform engineering.
• Proficient in Python and a systems language (Go/Rust/C++).
• Experience running high-throughput, low-latency services in production.
• Hands-on with LLM model inference frameworks (vLLM, TensorRT-LLM).
• Strong understanding of GPU architecture and accelerator utilization.
• Familiarity with Kubernetes, autoscaling, and cloud platforms.
• Experience with observability stacks (metrics, tracing, logging).
• Solid grounding in performance engineering and capacity planning.
• Strong incident response and communication skills.

Responsibilities

• Design and operate high-performance inference platforms for large ML models.
• Build scalable serving systems with efficient routing and batching.
• Optimize for latency, throughput, cost, and quality in AI serving.
• Ensure observability across model workloads and deployments.
• Collaborate with teams to ship reliable AI services at scale.

Skills

Python

Go/Rust/C++

Distributed systems

GPU/accelerators

Education

Bachelor’s or Master’s in CS

Tools

Kubernetes

TensorRT-LLM

vLLM

Job description

Bright Vision Technologies seeks a Model Serving Engineer to design, build, and operate high-performance inference platforms for serving large machine learning models in production. Focus is on the systems engineering side of AI deployment, including routing, batching, caching, autoscaling, GPU utilization, and end-to-end observability.

The ideal candidate brings strong distributed systems and performance engineering expertise, has shipped serving systems at scale, and understands