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# ML Systems Engineer

- **Company:** Bright Vision Technologies
- **Location:** Bedford, TX, United States (Remote available)
- **Experience:** Expert
- **Salary:** $145,000.0 - $165,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Systems Engineering, C++ (Programming Language), Distributed Systems, Python (Programming Language), Machine Learning, Azure Machine Learning, Software Deployment, Data Logging, Graphics Processing Unit (GPU), Autoscaling, Large Language Models, Caching, Kubernetes, Information Technology, Free and Open-Source Software, Machine Learning Operations, TensorRT
- **Published:** August 23, 2026
- **Apply:** https://www.careerjet.com/jobad/useff188e0212ae66338d9deaeb8965354

## About the Role

* Bachelor's or Master's degree in Computer Science or a related field.
 * Six or more years of experience in distributed systems, infrastructure, or ML platform engineering.
 * Strong proficiency in Python and a systems language such as Go, Rust, or C++.
 * Deep experience operating high-throughput, low-latency services in production.
 * Hands-on experience with LLM or large model inference frameworks such as vLLM or TensorRT-LLM.
 * Strong understanding of GPU architecture, memory hierarchies, and accelerator utilization.
 * Familiarity with Kubernetes, autoscaling, and modern cloud platforms.
 * Experience with observability stacks including metrics, tracing, and structured logging.
 * Solid grounding in performance engineering and capacity planning.
 * Strong communication and incident response skills., * Open-source contributions to model serving infrastructure.
 * Experience with multi-region or globally distributed AI serving.
 * Familiarity with model quantization, distillation, and compression techniques.
 * Exposure to FinOps for AI workloads and cost-efficient serving design.
 * Experience supporting external-facing AI APIs at scale.

## Description

We are seeking a ML Systems Engineer to design, build, and operate high-performance, highly reliable inference platforms for serving large machine learning models in production. The role focuses on the systems engineering side of AI deployment, including request routing, batching, caching, autoscaling, GPU utilization, and end-to-end observability across diverse model workloads. The ideal candidate brings strong distributed systems and performance engineering expertise, has shipped serving systems at scale, and understands the trade-offs between latency, throughput, cost, and quality in ML serving.


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