AI Systems Performance Specialist

Location

• 100% Remote (Continental United States) Position Type: Full-time, Direct W2 Salary Range: $130,000–$180,000 Annually Experience Required: 10+ 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

Key Responsibilities

• Optimize AI training and inference pipelines for maximum throughput, low latency, scalability, and infrastructure efficiency.
• Analyze and improve GPU utilization, memory management, kernel execution, and multi-GPU performance across production AI workloads.
• Design and implement optimization techniques including quantization, pruning, mixed precision, batching, caching, speculative decoding, and model parallelism .
• Profile AI applications using industry-standard performance analysis tools and identify bottlenecks across compute, memory, networking, and storage.
• Optimize distributed training and inference using NCCL, DeepSpeed, PyTorch Distributed, Ray, MPI, or similar distributed computing frameworks .
• Collaborate with AI researchers, ML engineers, platform engineers, and infrastructure teams to improve model performance and production reliability.
• Build automated benchmarking frameworks, performance dashboards, monitoring solutions, and regression testing pipelines.
• Evaluate emerging AI hardware, GPU architectures, inference frameworks, and optimization technologies to improve enterprise AI capabilities.
• Drive AI infrastructure cost optimization through efficient resource utilization, cloud optimization, and FinOps best practices.
• Mentor engineering teams and provide technical leadership on AI systems architecture, GPU optimization, and performance engineering.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, Artificial Intelligence, or a related technical discipline.
• 10+ years of professional experience in performance engineering, AI infrastructure, machine learning systems, High-Performance Computing (HPC), or distributed computing.
• Expert-level programming skills in Python and C++ .
• Extensive experience optimizing GPU-accelerated AI workloads using CUDA , distributed training frameworks, and modern deep learning libraries.
• Strong knowledge of Large Language Models (LLMs), deep learning frameworks, model serving, and production AI inference.
• Hands-on experience with profiling tools such as NVIDIA Nsight Systems, Nsight Compute, PyTorch Profiler, TensorBoard, or similar performance analysis tools .
• Experience deploying and optimizing AI workloads on AWS, Microsoft Azure, or Google Cloud Platform (GCP) .
• Strong understanding of distributed systems, networking, storage optimization, and AI infrastructure architecture.
• Excellent analytical, troubleshooting, communication, and technical leadership skills.

Preferred Qualifications

• Experience optimizing production-scale LLM inference and serving large foundation models.
• Hands-on experience with vLLM , TensorRT-LLM , DeepSpeed , Triton Inference Server , CUTLASS , FasterTransformer , or similar AI optimization frameworks.
• Knowledge of model compression, KV cache optimization, speculative decoding, and advanced inference optimization techniques.
• Experience implementing FinOps strategies for AI infrastructure cost optimization and resource management.
• Contributions to AI systems research, open-source AI infrastructure projects, patents, or technical publications.
• Familiarity with emerging AI accelerator technologies, including AMD ROCm , Intel oneAPI , or custom AI hardware.

Interested in this opportunity? Apply today for immediate consideration! Email your updated resume: [email protected] Call or Text: (908) 505-3545 Learn more: www.bvteck.com