• Develop and maintain real-time and historical performance monitoring tools for AI inference workloads, including profiling tools for various AI model types (small models, LLMs, VLMs, and multimodal systems) in applications like conversational AI, video processing, and real-time analytics.
• Analyze and classify inference workloads based on characteristics like profile, decode, pre/post-processing overheads, and computational complexity to develop tailored optimization strategies.
• Develop performance models that consider the systematic factors of AI inference, including model size, architecture (e.g., transformers, CNNs), application-specific constraints (e.g., latency for conversational AI), and compute resource characteristics (GPU, TPU, CPU, and specialized accelerators).
• Optimize inference workloads across various hardware resources by reducing latency, minimizing memory overhead, and improving throughput. Techniques include quantization, pruning, fusion, and caching. Ensure that models can scale efficiently across diverse compute platforms, from edge devices to large-scale cloud infrastructures.
• Lead efforts in creating benchmarks for different types of inference tasks. Utilize tools such as NVIDIA Nsight, PyTorch Profiler, and TensorBoard to gain insights into inference performance across diverse hardware platforms.
• Conduct benchmarking and performance comparisons across various hardware platforms (e.g., GPUs, TPUs, edge accelerators) to identify bottlenecks and optimization opportunities. Provide recommendations for software and hardware improvements based on inference throughput, latency, and power consumption.
• Work closely with AI research, software engineering, and DevOps teams to improve the end-to-end AI inference pipeline, ensuring optimized deployments across different production environments. Collaborate with system architects to incorporate resource-aware optimizations into design practices.
• Develop strategies to ensure the scalability of inference workloads in production environments, considering both model performance and resource scaling, whether in on-premises environments, cloud infrastructure, or edge computing devices.

Requirements

What you’ll bring to the team:

• Ph.D. or Master’s degree in Computer Science, Electrical Engineering, Machine Learning, or related field.
• Minimum 5+ years of experience in AI/ML engineering with a focus on inference performance, workload analysis, and system optimization.
• Extensive experience with AI frameworks (e.g., TensorFlow, PyTorch, ONNX) and model optimizationtechniques (e.g., quantization, pruning, kernel fusion, and hardware-aware tuning).
• Proficient with profiling tools (e.g., TensorBoard, PyTorch Profiler, NVIDIA Nsight) and workload analysis for diverse AI models and applications.
• Expertise in optimizing small models, large language models (LLMs), VLMs, and multimodal models for inference.
• Strong programming skills in Python, C++, CUDA, and experience with low-level hardware performance tuning.
• Familiarity with performance modeling methodologies and frameworks for predicting inference workload performance under varying conditions.
• Proven expertise in data parallelism, model parallelism, pipeline parallelism, and other distributed systems for performance improvements at scale.