ML Systems Engineer

ML Systems EngineerBright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential. Location: 100% Remote (U.S.) Position Type: Full-time Salary Range: $145,000–$165,000 Annually Experience Required: 6+ years 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.Job Summary: 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.Required QualificationsBachelor'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.Preferred QualificationsOpen-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.