Deep Learning Hardware Engineer (AI)

Location: Hybrid, with on-site work at Intel sites in Santa Clara, California, or Hillsboro, Oregon, United States.

Annual salary: $149,750–$240,710 USD.

Company

Develops client computing technologies and AI hardware for AI-enabled PCs and intelligent computing platforms.

What you will do

• 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 improvements.
• Collaborate with architecture, software, compiler, algorithm, and machine learning teams to define hardware capabilities and optimize solutions across the technology stack.
• Evaluate neural network mapping strategies for accelerated inference execution.
• Apply quantization, compression, pruning, sparsity, pipelining, parallelism, memory hierarchy, and data movement optimization techniques; contribute to invention disclosures, patents, conference papers, and publications.

Requirements

• Bachelor’s degree in Computer Science, Electrical Engineering, or a STEM-related field with 3+ years of relevant experience; or a Master’s degree in the same fields with 2+ years of experience; or a PhD in the same fields with 3+ months of educational or work experience.
• Experience in digital logic design and RTL design using SystemVerilog or Verilog.
• Experience with AI model architectures, layers, activation functions, and neural network topologies, including models such as Llama, Qwen, DeepSeek, or Pi0.5.
• Experience working with Git workflows and repositories.
• Experience optimizing AI models for performance through quantization, pruning, and compression.
• Curiosity, learning agility, analytical problem-solving, collaboration, ownership, adaptability, clear communication, and an innovation mindset.

Nice to have

• 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 multi-core and multi-threading architectures, memory hierarchy, and floating-point versus fixed-point computation.
• Experience collaborating across disciplines to align hardware and software requirements.

Culture & Benefits

• Hybrid work model with time split between the assigned site and off-site work.
• Health, retirement, and vacation benefits.
• Competitive pay and stock bonuses.
• Opportunities to work with AI and machine learning technologies, influence next-generation AI silicon architecture, and collaborate with hardware, software, and research teams.