Neural Network Integration Software Team Leader

Description

About Vision Business Unit Software Organization

The Vision Business Unit (VBU) at Ceva combines a dynamic startup environment with a stable, well-positioned IP company. Our main product is an NPU that supports LLMs. Our goal is to create and supply cutting-edge embedded software and graph compiler cores for AI. We continuously push technological limits to enable widespread access to technologies like augmented reality, virtual reality, 3D processing, Generative AI, and transformers.

Job Overview:

We are looking for a Software Team Leader to drive the integration and deployment of advanced neural networks — including GenAI LLMs, LVMs, and multimodal architectures — onto Ceva’s NPU-based platforms. You will lead a cross-functional team of engineers, working at the intersection of AI model design, embedded system optimization, and low-level hardware-software co-design.

This role is ideal for someone passionate about making real AI models run efficiently and robustly on edge hardware, and who thrives in a hands-on, collaborative technical environment.

 Key Responsibilities:

Lead and manage the Network Integration Software team, ensuring high performance and productivity. Oversee the design, development, and implementation. Collaborate with cross-functional teams to integrate solutions. Provide technical guidance and mentorship to team members. Ensure project timelines and deliverables are met. Conduct performance reviews and support professional development of team members. Collaborate globally with architects, HW teams, NPU teams, and other developer teams.

Requirements

• B.Sc in Engineering, Computer Science, or related technical field 
• At least 3 years of experience as Software Team Leader
• At least 5 years of hands-on C++ software engineering in embedded/firmware domains
• Experience in developing complex Embedded software systems
• Excellent problem-solving skills and attention to detail.
• Proficiency in performance profiling and optimization for inference workloads
• Strong communication and interpersonal skills.
• Ability to work collaboratively in a fast-paced environment.

Advantages

• Experience in deploying AI/ML models on edge devices (NPU, DSP, or other AI accelerators).
• Hands-on proficiency in TensorFlow, PyTorch, ONNX, TFLite.
• Experience with LLMs, Computer Vision, and Multimodal/Large Vision Models