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Deep Learning Hardware Engineer
Intel Corporation. Contribute to the design and optimization of neural compute and AI accelerator architectures .
About the role
Key responsibilities & impact- 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 quantization, compression, pruning, and sparsity techniques to improve AI efficiency
- Apply 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
- Collaborate across hardware, software, and research teams to build neural compute technologies for future AI-enabled PCs and intelligent computing platforms
Requirements
What you’ll need- 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, including Llama, Qwen, DeepSeek, and Pi0.5
- Experience working with Git workflows and repositories
- Experience optimizing AI models for performance, including quantization, pruning, and compression
- 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 versus fixed-point computations
- Experience collaborating in cross-disciplinary teams to align hardware and software requirements
Benefits
Comp & perks- Competitive pay
- Stock bonuses
- Health benefits
- Retirement benefits
- Vacation benefits
- Hybrid work model allowing employees to split time between working on-site and off-site