Machine Learning Infrastructure Engineer

Bright Vision Technologies • Artesia, CA

1d • $105,000 - $143,000 • Remote

About The Position

Bright Vision Technologies is seeking a Machine Learning Infrastructure 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 has strong distributed systems and performance engineering expertise, has shipped serving systems at scale, and understands trade-offs between latency, throughput, cost, and quality in ML serving.

This is a full-time, direct W2 position that is 100% remote in the United States. The annual salary range is $105,000–$143,000. U.S. citizens, Green Card holders, EAD holders, and H-1B transfer candidates are encouraged to apply; the company cannot sponsor new H-1B visa petitions for this position.

Requirements

• 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.

Nice To Haves

• 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.

Responsibilities

• Design, build, and operate high-performance, highly reliable inference platforms for production machine learning models.
• Address systems engineering needs for AI deployment, including request routing, batching, caching, autoscaling, GPU utilization, and end-to-end observability.
• Support diverse model workloads and balance latency, throughput, cost, and quality in ML serving.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior