Deep Learning Hardware Engineer

The Role and Impact Intel's Client AI Architecture team is seeking an Deep Learning Hardware Engineer to help shape future generations of Intel's Neural Processing Unit (NPU) architecture for AI PCs, Edge AI, Agentic AI, Physical AI, and multimodal foundation models. In this role, you will work at the intersection of AI, computer architecture, and hardware-software co-design to optimize AI workloads and drive innovations in next-generation AI acceleration technologies.

You will collaborate with architecture, software, compiler, and machine learning teams to develop high-performance, power-efficient AI solutions for Intel client platforms.

The ideal candidate has experience developing, optimizing, or deploying AI workloads on NPUs, GPUs, AI accelerators, or related computing architectures. Experience with Intel NPUs, OpenVINO, AI software stacks, compiler technologies, AI inference optimization, and hardware-software co-design is highly desirable.

• Contribute to the design and optimization of neural compute and AI accelerator architectures.
• Develop and evaluate RTL implementations of hardware features supporting machine learning workloads.
• Analyze AI models and workloads to identify performance, power, and efficiency opportunities.
• Partner with architecture, software, and algorithm teams to define future hardware capabilities.
• Support hardware-software co-design efforts to optimize AI solutions across the technology stack.
• Evaluate neural network mapping strategies for accelerated inference execution.
• Apply optimization techniques such as quantization, compression, pruning, and sparsity to improve AI efficiency.
• Utilize computer architecture principles including pipelining, parallelism, memory hierarchy, and data movement optimization.
• Participate in technical innovation activities, including invention disclosures, patents, conference papers, and publications.

• Curiosity & Learning Agility – Eager to learn new technologies, quickly absorb concepts, and continuously develop technical expertise.
• Problem Solving – Uses analytical thinking to break down complex challenges and develop practical, data-driven solutions.
• Collaboration – Works effectively across diverse teams, values different perspectives, and builds strong working relationships.
• Ownership & Initiative – Takes responsibility for deliverables, follows through on commitments, and proactively identifies opportunities for improvement.
• Adaptability – Thrives in a fast-paced environment, embraces change, and adjusts priorities as business needs evolve.
• Communication – Clearly communicates technical concepts, ideas, and recommendations to both technical and non-technical audiences.
• Innovation Mindset – Demonstrates creativity, challenges assumptions, and seeks new approaches to improve products, processes, and customer outcomes.

Intel invests in our people and offers a complete and competitive package of benefits employees and their families through every stage of life. See Intel Benefits for more details.

• Bachelor's degree in Computer Science, Electrical Engineering, or a STEM-related field with 3+ years of relevant experience; OR Master's degree in the same fields with 2+ years of experience; OR PhD degree in the same fields with 3+ months of educational or work experience.
• Digital Logic Design and RTL Design using System Verilog or Verilog.
• AI model architectures, layers, activation functions, and neural network topologies (e.g., Llama, Qwen, Deep Seek, Pi0.5).
• Experience working with Git workflows and repositories.
• Optimizing AI models for performance, such as quantization, pruning, and compression.

• Familiarity with AI…