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# ML Systems Engineer

- **Company:** Bright Vision Technologies
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $145,000.0 - $165,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, C++ (Programming Language), Distributed Systems, Python (Programming Language), Azure Machine Learning, Data Logging, Graphics Processing Unit (GPU), Autoscaling, Large Language Models, Kubernetes, Information Technology, Free and Open-Source Software, Machine Learning Operations, TensorRT
- **Published:** July 29, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=13c27dd88fb9ce5f

## About the Role

Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position., * 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.



## Related Videos

- [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated)
- [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops)
- [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way)
- [Fifty Shades of Kubernetes Autoscaling](https://www.wearedevelopers.com/videos/813-fifty-shades-of-kubernetes-autoscaling)
- [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms)
- [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow)

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- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)