Senior AI Performance Engineer

Brillfy Technology Inc; United States; On-site; USD 150,000 - 210,000; Full time

Job summary

Brillfy Technology Inc. is seeking a hands-on GPU/AI systems engineer focused on CUDA optimization, multi-GPU performance, and system-wide profiling.

The role requires deep expertise in GPU architecture and performance tuning, with experience across NVIDIA tools and distributed AI stacks. The ideal candidate will profile AI/ML workloads, identify bottlenecks, and implement latency and throughput enhancements across compute, memory, and networking layers, collaborating with AI, DevOps, and

Qualifications

• Strong hands-on CUDA programming and GPU performance optimization.
• Deep understanding of GPU architecture and memory hierarchy.
• Experience with Nsight and CUDA profiling tools.

Responsibilities

• Profile and optimize AI/ML workloads across multi-GPU and multi-node systems.
• Identify bottlenecks across compute, memory, networking, and orchestration layers.
• Optimize CUDA kernels (memory coalescing, shared memory usage, occupancy tuning).
• Improve inference performance using TensorRT, Triton, DeepStream, NeMo.
• Analyze and improve latency, throughput, GPU utilization, and memory efficiency.
• Work on distributed AI systems using Apache Ray, NCCL, Kubernetes GPU scheduling.
• Build benchmarking frameworks and performance monitoring systems.
• Collaborate with AI, DevOps, and Infrastructure teams for system-wide optimization.

Skills

CUDA programming

GPU architecture

Performance profiling

NVIDIA ecosystem (TensorRT, Triton, Ne

Tools

Nsight tools

Job description

Job Description

This is a hands-on engineering role, requiring deep expertise in CUDA, GPU architecture, and performance profiling.

Key Responsibilities

• Profile and optimize AI/ML workloads across multi-GPU and multi-node systems


• Identify bottlenecks across compute, memory, networking, and orchestration layers


• Optimize CUDA kernels (memory coalescing, shared memory usage, occupancy tuning)


• Improve inference performance using TensorRT, Triton, DeepStream, NeMo


• Analyze and improve latency, throughput, GPU utilization, and memory efficiency


• Work on distributed AI systems using Apache Ray, NCCL, Kubernetes GPU scheduling


• Build benchmarking frameworks and performance monitoring systems


• Collaborate with AI, DevOps, and Infrastructure teams for system-wide optimization

Required Skills

• Strong hands-on CUDA programming and GPU performance optimization


• Deep understanding of GPU architecture and memory hierarchy


• Experience with Nsight, CUDA profiling tools, performance benchmarking


• Hands-on experience with NVIDIA ecosystem (Triton, TensorRT, NeMo, DeepStream)


• Experience with distributed AI systems (multi-GPU, multi-node, NCCL, Ray)


• Experience working with AI models such as YOLO, GPT, LLaMA, Transformers


• Strong understanding of AI system performance metrics (latency, throughput, utilization)

Preferred

• Experience working at NVIDIA or similar GPU/AI infrastructure companies


• Experience with real-time video / Vision AI systems


• Experience with large-scale production AI deployments

Interview Process (Mandatory)

• Candidates will receive a technical handout 1 day before interview


• 90-minute deep-dive demo discussion (NOT theoretical)


• Candidate must explain:


• Bottleneck identification approach


• GPU optimization strategies


• System-level performance improvements