Senior Staff Engineer/Principal Engineer/Manager

Senior Staff Engineer/Principal Engineer/Manager - AIML/Hardware Accelerators

Job Area:
Engineering Group, Engineering Group > Systems Engineering

General Summary:

Minimum Qualifications:

Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 8+ years of Systems Engineering or related work experience.
OR Master's degree in Engineering, Information Systems, Computer Science, or related field and 7+ years of Systems Engineering or related work experience. OR PhD in Engineering, Information Systems, Computer Science, or related field and 6+ years of Systems Engineering or related work experience.

• 17+ years of experience in AI/ML development, with at least 5 years in model inference, optimization, debugging, and Python-based AI deployment.
• Masters or Ph.D. in Computer Science, Machine Learning, AI

• Lead a team of AI engineers in Python-based AI inference development .
• Collaborate with ML researchers, software engineers, and DevOps teams to deploy optimized AI solutions.
• Define and enforce best practices for debugging and optimizing AI models

• Optimize deep learning models using quantization (INT8, INT4, mixed precision etc), pruning, and knowledge distillation .
• Implement Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT) for deployment.
• Familiarity with TensorRT, ONNX Runtime, OpenVINO, TVM

• Optimize AI workloads for Qualcomm Hexagon DSP, GPUs (CUDA, Tensor Cores), TPUs, NPUs, FPGAs, Habana Gaudi, Apple Neural Engine .
• Leverage Python APIs for hardware-specific acceleration , including cuDNN, XLA, MLIR .
• Benchmark models on AI hardware architectures and debug performance issues

• Conduct state-of-the-art research on AI inference efficiency, model compression, low-bit precision, sparse computing, and algorithmic acceleration .
• Explore new deep learning architectures (Sparse Transformers, Mixture of Experts, Flash Attention) for better inference performance .
• Contribute to open-source AI projects and publish findings in top-tier ML conferences (NeurIPS, ICML, CVPR).
• Collaborate with hardware vendors and AI research teams to optimize deep learning models for next-gen AI accelerators.

• Experience optimizing LLMs, LVMs, LMMs for inference
• Experience with deep learning frameworks : TensorFlow, PyTorch, JAX, ONNX.
• Advanced skills in model quantization, pruning, and compression .
• Proficiency in CUDA programming and Python GPU acceleration using cuPy, Numba, and TensorRT .
• Hands-on experience with ML inference runtimes (TensorRT, TVM, ONNX Runtime, OpenVINO)
• Experience working with RunTimes Delegates (TFLite, ONNX, Qualcomm)
• Strong expertise in Python programming , writing optimized and scalable AI code.
• Experience with debugging AI models , including examining computation graphs using Netron Viewer, TensorBoard, and ONNX Runtime Debugger .
• Strong debugging skills using profiling tools (PyTorch Profiler, TensorFlow Profiler, cProfile, Nsight Systems, perf, Py-Spy) .
• Expertise in cloud-based AI inference (AWS Inferentia, Azure ML, GCP AI Platform, Habana Gaudi).
• Knowledge of hardware-aware optimizations (oneDNN, XLA, cuDNN, ROCm, MLIR, SparseML).

• Contributions to open-source community
• Publications in International forums conferences journals