Deep Learning Hardware Engineer (AI)

Location: Hybrid work model with on-site and off-site work; assigned hirify.global sites in Santa Clara, California, or Hillsboro, Oregon, United States.

Annual salary: $149,750–$240,710 USD.

Company

hirify.global develops computing, AI, neural processing, and semiconductor technologies for personal computers and hirify.globalligent computing platforms.

What you will do

• Design and optimize neural compute and AI accelerator architectures for next-generation hirify.global NPU technologies.
• Develop and evaluate RTL implementations of hardware features supporting machine learning workloads.
• Analyze AI models and workloads to identify performance, power, and efficiency opportunities.
• Collaborate with architecture, software, compiler, algorithm, and machine learning teams on hardware-software co-design.
• Evaluate neural network mapping strategies for accelerated inference execution.
• Apply quantization, compression, pruning, sparsity, pipelining, parallelism, memory hierarchy, and data movement optimization techniques.

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 degree 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, initiative, adaptability, and clear communication with technical and non-technical audiences.

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 concepts, memory hierarchy, and floating-point versus fixed-point computations.
• Experience collaborating across disciplines to align hardware and software requirements.

Culture & Benefits

• Hybrid work model combining on-site work at an assigned hirify.global site with off-site work.
• Health, retirement, and vacation benefits.
• Competitive pay, stock bonuses, and a total compensation package.
• Opportunities to work with AI and machine learning technologies and learn from hardware and AI experts.
• Collaboration across hardware, software, and research teams on next-generation AI silicon architecture.