Deep Learning Hardware Engineer

Job Title

Deep Learning Hardware Engineer

Role Summary

Design and optimize neural processing unit (NPU) and AI accelerator hardware for client and edge AI platforms. Work at the intersection of computer architecture, hardware-software co-design, compilers, and machine learning to improve performance and power efficiency for AI workloads.

Collaborate with architecture, software, compiler, and ML teams to define hardware capabilities, evaluate AI model mappings, and deliver optimized solutions for inference and related AI tasks.

Experience Level

Mid-level (typically 3+ years of relevant experience).

Responsibilities

Primary technical and cross-functional responsibilities include:

• 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 opportunities to improve performance, power, and efficiency.
• Partner with architecture, software, compiler, and algorithm teams to define and validate hardware capabilities.
• Support hardware–software co-design efforts across the technology stack.
• Evaluate neural network mapping and scheduling strategies for accelerated inference execution.
• Apply model optimization techniques such as quantization, pruning, compression, and sparsity.
• Apply computer architecture principles including pipelining, parallelism, memory hierarchy, and data movement optimization.
• Participate in technical innovation activities such as invention disclosures, patents, and conference publications.

Requirements

Core technical qualifications and desirable skills.

• Must-have: Digital logic and RTL design experience using SystemVerilog or Verilog.
• Must-have: Familiarity with AI model architectures, layers, activation functions, and neural network topologies.
• Must-have: Experience optimizing AI models for performance (quantization, pruning, compression) and analyzing workload performance trade-offs.
• Must-have: Experience with Git workflows and repositories.
• Must-have: Practical experience running or optimizing AI workloads on accelerators (NPUs, GPUs, or other AI accelerators).
• Nice-to-have: Experience with Intel NPUs, OpenVINO, AI software stacks, or compiler technologies.
• Nice-to-have: Hands-on experience with foundational models and modern deep-learning topologies.
• Nice-to-have: In-depth knowledge of computer architecture concepts (multi-core/multi-threading, memory hierarchy, floating-point vs fixed-point).
• Nice-to-have: Experience collaborating across hardware, software, and algorithm teams to align requirements.

Education Requirements

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 in the same fields with at least several months of related educational or work experience. Qualifications may also be met through a combination of industry experience, internships, coursework, or research.

About the Company

Company: Intel Corporation

Headquarters: Santa Clara, California, USA

Intel Corporation is a leading multinational technology company known for its innovative semiconductor solutions, including microprocessors, artificial intelligence accelerators, and memory products. Headquartered in the United States, Intel focuses on cutting-edge technology and a collaborative working environment, driving advancements in semiconductor manufacturing to meet global demands. The company emphasizes professional development and aims to shape the future of technology through groundbreaking designs.