Job Description

Job Title: Senior AI Performance Engineer (CUDA / GPU / NVIDIA Stack)

Duration: Min 12+ Months

Location: 100% Remote

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

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