Deep Learning Hardware Engineer

Intel Corporation; Santa Clara (CA); Hybrid; USD 150,000 - 241,000; Full time

Job summary

Intel is seeking a Deep Learning Hardware Engineer to shape the next generation of NPU architecture for AI PCs, edge AI, and multimodal foundation models. You will work at the intersection of AI, computer architecture, and hardware-software co-design to optimize workloads and drive innovations in AI acceleration.

The role collaborates across architecture, software, compiler, and ML teams to deliver high-performance, power-efficient AI solutions for client platforms, with emphasis on

Qualifications

• Bachelor's degree with 3+ years of relevant experience or Master's with 2+ years or PhD with 3+ months.
• Experience with digital logic design and RTL using SystemVerilog or Verilog.
• Experience with AI model architectures and neural network topologies.

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, pruning, and compression to improve AI efficiency.
• Utilize computer architecture principles including pipelining, parallelism, memory hierarchy, and data movement optimization.

Skills

SystemVerilog/Verilog

AI model architectures

Git workflows

AI model optimization

Education

Bachelor's degree in CS/EE or STEM field

Master's degree in same fields

PhD in same fields

Job description

The Role and Impact Intel's Client AI Architecture team is seeking an 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. In this role, you will work 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. You will collaborate with architecture, software, compiler, and machine learning teams to develop high-performance, power-efficient AI solutions for Intel client platforms.

The ideal candidate has experience developing, optimizing, or deploying AI workloads on NPUs, GPUs, AI accelerators, or related computing architectures. Experience with Intel NPUs, OpenVINO, AI software stacks, compiler technologies, AI inference optimization, and hardware-software co-design is highly desirable.

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.

Behavioral Traits

• Curiosity & Learning Agility – Eager to learn new technologies, quickly absorb concepts, and continuously develop technical expertise.
• Problem Solving – Uses analytical thinking to break down complex challenges and develop practical, data-driven solutions.
• Collaboration – Works effectively across diverse teams, values different perspectives, and builds strong working relationships.
• Ownership & Initiative – Takes responsibility for deliverables, follows through on commitments, and proactively identifies opportunities for improvement.
• Adaptability – Thrives in a fast-paced environment, embraces change, and adjusts priorities as business needs evolve.
• Communication – Clearly communicates technical concepts, ideas, and recommendations to both technical and non-technical audiences.
• Innovation Mindset – Demonstrates creativity, challenges assumptions, and seeks new approaches to improve products, processes, and customer outcomes.

Intel invests in our people and offers a complete and competitive package of benefits employees and their families through every stage of life. See Intel Benefits for more details.

Qualifications

Minimum Qualifications and Experience

• 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.
• 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 and Experience

• 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.

Why Join Us

The Client Computing Group (CCG) develops industry-leading technologies that power personal computing experiences around the world. 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.

Job Type

Job Type: College Grad Shift: Shift 1 (United States of America) Primary Location: US, California, Santa Clara Additional Locations: US, Oregon, Hillsboro

Posting Statement

All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation or any other characteristic protected by local law, regulation, or ordinance.

Position of Trust: N/A

Benefits We offer a total compensation package that ranks among the best in the industry. It consists of competitive pay, stock bonuses, and benefit programs which include health, retirement, and vacation. Find out more about the benefits of working at Intel.

Annual Salary Range

Annual Salary Range for jobs which could be performed in the US: $149,750.00-240,710.00 USD The range displayed on this job posting reflects the minimum and maximum target compensation for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.

Work Model for this Role

This role will be eligible for our hybrid work model which allows employees to split their time between working on-site at their assigned Intel site and off-site. * Job posting details (such as work model, location or time type) are subject to change.