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 be a key contributor to the design and implementation of Ceva’s AI Graph Compiler software stack for Neural Processing Units (NPUs). You will take part in defining software architecture, implementing performance-critical components, and enabling efficient execution of advanced neural networks under tight power, memory, and latency constraints.
You will work closely with hardware and system architects, software and hardware engineers, influencing both software and hardware decisions. You will design and implement major parts of Ceva NPU embedded solutions, actively promoting Ceva AI capabilities to the customers.
What will you do:
Own and design key components of the AI Graph Compiler software stack for NPU-based systems.
Lead optimization of inference performance (latency, throughput, memory footprint, power) for edge deployments
Collaborate on HW–SW co-design, influencing NPU architecture
Support IP evaluations and silicon bring-up, root-cause complex HW/SW issues, and influence development methodologies
Mentor junior engineers and contribute to technical best practices
Requirements- 7+ years of experience in building high-quality embedded software using C/C++.
- BSc/MSc in Computer Science, Electrical Engineering, or equivalent.
- Experience developing and maintaining embedded systems, including multi-component software stacks, tight HW/SW integration, and system-level debugging
- Experience in designing and implementing software based on product & hardware specifications
- Experience working under tight memory, power, and real-time constraints
- Strong English proficiency with the ability to communicate clearly and effectively in writing and verbally.
- Advantages:
- Proficiency in Python coding
- Experience in data-flow optimization using profiling tools
Skills Required
- 7+ years of experience in building high-quality embedded software using C/C++
- BSc/MSc in Computer Science, Electrical Engineering, or equivalent
- Experience developing and maintaining embedded systems
- Experience in designing and implementing software based on product & hardware specifications
- Experience working under tight memory, power, and real-time constraints
- Strong English proficiency with clear communication skills
What We Do
Ceva powers the Smart Edge, bridging the digital and physical worlds to bring AI-driven products to life. Our Ceva AI fabric portfolio of silicon and software IP enables devices to Connect, Sense, and Infer – the essential capabilities for the intelligent edge. From 5G, cellular IoT, Bluetooth, Wi-Fi, and UWB connectivity to scalable Edge AI NPUs, AI DSPs, sensor fusion processors and embedded software, Ceva provides the foundational IP for devices that connect, understand their environment, and act in real time. With more than 20 billion devices shipped and trusted by 400+ customers worldwide, Ceva is the backbone of today’s most advanced smart edge products – from AI-infused wearables and IoT devices to autonomous vehicles and 5G infrastructure. Our differentiated solutions deliver seamless integration into existing design flows, total flexibility to combine solutions based on design needs and ultra–low–power performance in minimal silicon footprint, helping customers accelerate development, reduce risk, and bring innovative products to market faster. As technology evolves toward Physical AI, Ceva’s IP portfolio lays the foundation for systems that are always connected, contextually aware, and capable of intelligent, real-time decision-making.







