AI Python Developer
Summary
Ceva's AI division in Ra'anana is hiring a Python developer to research LLM/generative-AI architectures and build hardware-aware quantization tooling for deploying neural networks on Ceva's edge-AI hardware (NPUs and Vision DSPs). Day to day: network deployment, accuracy measurement/improvement, and graph-compiler quantization work, mainly in Python and PyTorch.
About the AI Division
The AI Division is a unique and dedicated group within Ceva, driving innovation in Machine Learning and Generative AI architectures for edge devices and cloud inference.
Our R&D domains span Neural Network Processors (NPU), Vision DSPs, and advanced AI algorithms for applications across smartphones, tablets, automotive, surveillance cameras and many more edge AI systems.
We combine cutting-edge hardware IP design with embedded software and system-level solutions, enabling the next generation of intelligent and energy-efficient devices.
About the Role:
In this role, you will build deep expertise in AI fundamentals, LLM and generative AI network architectures, and their underlying algorithms. You will continuously research emerging generative AI architectures across the industry, staying ahead of how the field evolves.
A central focus will be quantization - understanding, developing, and refining quantization methods and algorithms, and driving their deployment on our hardware to achieve maximum performance without compromising accuracy.
Responsibilities:
Explore and plan end to end network deployment on Ceva's hardware.
Own network accuracy measurement and improvement.
Develop the graph compiler's hardware-aware quantization tool.
Requirements
- B.Sc/M.Sc. in Engineering, Computer Science, or related technical field.
- 3-4 years of experience as a Python developer.
- Experience working with PyTorch.
- Experience evaluating and running ML/AI models, with knowledge of common architectures.
- Proficient in using AI tools, backed by solid testing practices.
- Strong communication skills, self-driven, with solid time management.
Advantages:
- Experience with LLMs.
- Experience with networks quantization methods.
- Experience with HuggingFace, PyTest and Jenkins.