Qualcomm Systems Engineer & Principal AI/ML Engineer
Company Overview
Qualcomm India Private Limited is a leading technology innovator dedicated to pushing the boundaries of what's possible.
Job Roles & Responsibilities
General Overview
As a Systems Engineer or Principal AI/ML Engineer, you'll be involved in the research, design, development, simulation, and validation of systems-level software, hardware, architecture, algorithms, and solutions.
Key Responsibilities
- Collaborate across teams to meet and exceed system requirements
- Build and optimize AI inference systems
- Debug deep learning models
- Accelerate AI workloads for low latency
- Deploy AI models across diverse hardware platforms
- Lead AI engineering teams
- Define best practices for AI model debugging and optimization
- Conduct cutting-edge research in AI inference efficiency and model compression
- Explore new architectures and contribute to open-source AI projects
Qualifications
- Bachelor's Degree in Engineering, Computer Science, or related field + 8+ years experience
- OR Master's Degree + 7+ years experience
- OR PhD + 6+ years experience
- 20+ years of AI/ML development experience
- Expertise in model inference, optimization, debugging, and Python deployment
- Master's or PhD in relevant fields
Technical Skills & Experience
- Model Optimization & Quantization (INT8, INT4, mixed precision, pruning, distillation)
- Hardware Acceleration (Hexagon DSP, GPUs, TPUs, NPUs, FPGAs, Gaudi, Neural Engine)
- Deep Learning Frameworks (TensorFlow, PyTorch, JAX, ONNX)
- CUDA & Python GPU Acceleration (cuPy, Numba, TensorRT)
- ML Inference Runtimes (TensorRT, TVM, ONNX Runtime, OpenVINO)
- Cloud-based AI inference (AWS, Azure, GCP, Habana)
- Debugging tools & profiling (PyTorch Profiler, TensorFlow Profiler, Nsight, perf, Py-Spy)
- Open-source contributions and publication in top-tier ML conferences
Benefits & Opportunities
- Work with cutting-edge AI hardware and software
- Lead innovative projects
- Collaborate with global researchers and engineers
Tags
AI, Machine Learning, Deep Learning Frameworks, Hardware Acceleration, Model Optimization