In this role, you will be a key contributor to the design and implementation of our companys Edge AI 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 our company NPU embedded solutions, actively promoting our company AI capabilities to the customers.
What will you do:
Own and design key components of the Edge AI software stack for NPU-based systems.
Optimize 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.
Proven experience developing and maintaining complex embedded systems, including multi-component software stacks, tight HW/SW integration, and system-level debugging.
Proven record of technical leadership in complex embedded systems development, influencing cross-team design or architectural decisions.
Experience in designing and implementing software based on product & hardware specifications.
Experience in data-flow optimization using profiling tools.
Experience working under tight memory, power, and real-time constraints.
Excellent interpersonal and communication skills, with a proven ability to work well in a team.
Advantages:
Interaction with AI compilers / graph optimizers.
Familiarity with fixed-point / quantized inference is a strong plus.
Experience with customer-facing enablement (SDKs, documentation, integration support).
Familiarity with neural network open-source frameworks such as PyTorch and TensorFlow.
Proficiency in Python coding.
Knowledge in computer vision and machine learning.
This position is open to all candidates.