Deep Learning Hardware Engineer
Intel
About the Role
Intel's Client AI Architecture team is seeking a 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. This role operates 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.
Key Responsibilities
- 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.
Requirements & Tech Stack
Minimum Qualifications
- 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.
- Experience with AI model architectures, layers, activation functions, and neural network topologies (e.g., Llama, Qwen, DeepSeek, Pi0.5).
- Experience working with Git workflows and repositories.
- Optimizing AI models for performance, such as quantization, pruning, and compression.
Preferred Qualifications
- Familiarity with AI frameworks, neural computing operations, and hardware-accelerated inference techniques.
- Hands-on experience with modern deep learning topologies, including foundational models.
- In-depth knowledge of computer architecture concepts such as multi-core/multi-threading, memory hierarchy, and floating-point vs. fixed-point computations.
- Experience collaborating in cross-disciplinary teams to align hardware and software requirements.
Qualifications
Experience listed will be obtained through a combination of industry relevant job experience, internship experiences and/or schoolwork/classes/research.
Why Join Us
Within Intel's AI Hardware Engineering organization, you'll help build the neural compute technologies that drive future AI-enabled PCs and intelligent computing platforms.
This Role Provides Opportunities To
- Work on cutting-edge AI and machine learning technologies.
- Learn from industry-leading hardware and AI experts.
- Influence next-generation AI silicon architecture.
- Collaborate across hardware, software, and research teams.