Remote ML Systems Engineer – Scalable Inference

Bright Vision Technologies; Chesterfield (MO); Remote; USD 145.000 - 165.000; Vollzeit

Zusammenfassung

Bright Vision Technologies is seeking an ML Systems Engineer to design, build, and operate high-performance inference platforms for serving large ML models in production, focusing on systems engineering of AI deployment.

You will handle request routing, batching, caching, autoscaling, GPU utilization, and end-to-end observability, balancing latency, throughput, and cost while shipping reliable serving systems at scale.

Qualifikationen

• Bachelor’s or Master’s degree in Computer Science or related field.
• 6+ years in distributed systems, infrastructure, or ML platform engineering.
• Proficient 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).
• Strong GPU knowledge and accelerator utilization.
• Familiar with Kubernetes, autoscaling, and cloud platforms.
• Observability stack experience (metrics, tracing, logging).
• Knowledge in performance engineering and capacity planning.
• Strong communication and incident response skills.

Aufgaben

• Design, build, and operate high-performance ML inference platforms.
• Focus on systems engineering for AI deployment, including routing, batching, caching, autoscaling.
• Ensure end-to-end observability across diverse model workloads.
• Balance latency, throughput, and cost while maintaining reliability.

Kenntnisse

Distributed systems

Performance engineering

Incident response

Communication skills

Cloud platforms

Ausbildung

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

Tools

Python

Go

Rust

C++

Kubernetes

TensorRT-LLM

vLLM

Jobbeschreibung

Bright Vision Technologies is seeking an ML Systems Engineer to design, build, and operate high-performance inference platforms for serving large ML models in production, focusing on systems engineering of AI deployment.

You will handle request routing, batching, caching, autoscaling, GPU utilization, and end-to-end observability, balancing latency, throughput, and cost while shipping reliable serving systems at scale.