AI/ML Infrastructure Engineer

Required Qualifications

• BS/MS or Ph.D. in Computer Science, Electrical Engineering, or a related discipline.
• Strong programming skills in C/C++ and Python.
• Experience with model optimization, quantization, and efficient deep learning techniques (e.g., knowledge distillation, pruning).
• Deep understanding of GPU hardware performance, including execution models, thread hierarchy, memory/cache management, and the cost/performance trade-offs of video processing.
• Experience with profiling and benchmarking tools (e.g., Nsight Systems, Nsight Compute) to validate performance on complex architectures.
• Experience identifying and resolving compute and data flow bottlenecks, particularly in high-bandwidth video processing pipelines.
• Strong communication skills and the ability to work cross-functionally between research and infrastructure teams.
• BS/MS or Ph.D. in Computer Science, Electrical Engineering, or a related discipline.
• Strong programming skills in C/C++ and Python.
• Experience with model optimization, quantization, and efficient deep learning techniques (e.g., knowledge distillation, pruning).
• Deep understanding of GPU hardware performance, including execution models, thread hierarchy, memory/cache management, and the cost/performance trade-offs of video processing.
• Experience with profiling and benchmarking tools (e.g., Nsight Systems, Nsight Compute) to validate performance on complex architectures.
• Experience identifying and resolving compute and data flow bottlenecks, particularly in high-bandwidth video processing pipelines.
• Strong communication skills and the ability to work cross-functionally between research and infrastructure teams.
• Familiarity with database systems (e.g., SQL, Neo4j).
• Work in Computer Vision, Deep Learning, and Vision Transformers.
• Experience with video processing frameworks such as NVIDIA DeepStream, DALI, or FFmpeg.
• Familiarity with ML compilers (e.g., TVM, MLIR) or inference engines like TensorRT or ONNX Runtime.
• Knowledge of distributed training systems or cloud-scale inference serving (e.g., Triton Inference Server).
• Familiarity with database systems (e.g., SQL, Neo4j).
• Work in Computer Vision, Deep Learning, and Vision Transformers.
• Experience with video processing frameworks such as NVIDIA DeepStream, DALI, or FFmpeg.
• Familiarity with ML compilers (e.g., TVM, MLIR) or inference engines like TensorRT or ONNX Runtime.
• Knowledge of distributed training systems or cloud-scale inference serving (e.g., Triton Inference Server).