DevJobs

AI Software Architect

Overview
Skills
  • C++ C++
  • Python Python
  • PyTorch PyTorch
  • AI
  • Embedded processors
  • Vision
  • xla
  • vLLM
  • tvm
  • Tensor Flow
  • SoCs
  • SgLang
  • Optimum
  • Onnx runtime
  • NPUs
  • Neural networks
  • MLIR iree
  • mlir
  • LLMs
  • LLM quantization algorithms
  • Generative AI
  • ExecuTorch
  • DSPs
  • CNN quantization algorithms

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 shaping the future of Ceva’s AI software stack for Neural Processing Units (NPUs). You will lead software architecture, define performance-critical components, and enable 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.

We are seeking a high-impact motivated Software Architect to join our team and help shape the future of our advanced neural network AI Software Toolchain over Ceva’s AI computing processors.


Responsibilities

Lead Software Architecture specification and supervise design for the most advanced Software Toolchain handling state-of-the-art Neural Processing Unit operation.

Design and promote holistic software solutions incorporating various aspects of NPU handling: from network retraining, graph compiling and network inference execution on the embedded systems.

Collaborate proactively with Product, Architecture, VLSI and Software teams to promote software leadership over various markets and compute SoCs.

Explore and translate state-of-the-art neural network and AI applications requirements into software architecture flows, encompassing hardware, software, tools and other components.

Evaluate architecture proposals, internal and external IP features and provide influential and inspirational leadership across hardware and software to align all parties to a common vision of architecture & technology development.

Represent Ceva with high technical credibility in customer meetings, appropriately incorporating feedback.

Boost velocity of development teams by providing technical guidance and by constantly looking ahead to anticipate and resolve future challenges.

Conduct experiments, invent and drive development of supporting tools such as simulators, models, profilers, and other methods as required.

Engage with engineering leadership and product planning stakeholders to develop technology roadmap.


Requirements:

  • B.Sc in Engineering, Computer Science, or related technical field. MS or PhD highly desired.
  • 5+ years of experience as SW Architect.
  • 8+ years of experience as SW developer.
  • Proficiency in Python, C++.
  • Proven track record in Software Architecture development, maintenance and improvement over embedded processors in AI and vision domains.
  • Excellent communication skills, both verbally and in writing. Collaborative and influential across organizations.
  • Proven ability to advance initiatives effectively in ambiguous and dynamic environments.
  • Ability to work and operate in a highly dynamic environment.


Advantages:

  • Experience in implementing state-of-the-art neural networks, generative AI and LLMs embedded SW over SoCs /DSPs/NPUs.
  • Familiarity with AI frameworks such as optimum PyTorch, Tensor Flow, vLLM, SgLang and others.
  • Familiarity with graph compilers such as mlir, tvm, xla.
  • Familiarity with runtime frameworks such as ExecuTorch, MLIR iree, Onnx runtime.
  • Deep understanding of LLM and CNN quantization algorithms.

CEVA