Role Description

Technical > AI/ML Chip Design Specialist

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Short Description:

An AI/ML Chip Design Specialist is responsible for designing and optimizing hardware architectures specifically tailored for artificial intelligence and machine learning applications. They develop and test high-performance chips that improve processing speed, energy efficiency, and computational capabilities for AI workloads. The specialist collaborates with hardware engineers, software developers, and data scientists to ensure seamless integration between hardware and AI algorithms. They also analyze performance metrics, troubleshoot design issues, and implement improvements to enhance chip functionality. Strong expertise in semiconductor design, digital architecture, and AI acceleration technologies is essential for success in this role.

Duties / Responsibilities:

  • Design and develop high-performance AI/ML hardware accelerators and chip architectures optimized for machine learning workloads
  • Collaborate with hardware and software engineers to integrate AI algorithms into chip designs for maximum computational efficiency
  • Conduct simulations, performance modeling, and verification to validate chip functionality and power efficiency
  • Optimize hardware for neural network processing, including tensor operations, matrix multiplications, and memory management
  • Implement and refine hardware-software co-design strategies for AI inference and training applications
  • Utilize EDA (Electronic Design Automation) tools for synthesis, layout, and verification of AI chip components
  • Analyze bottlenecks in data flow, memory access, and computation pipelines to enhance performance and scalability
  • Collaborate with firmware and systems engineers to ensure seamless deployment of AI chips in embedded systems or data centers
  • Stay current with emerging trends in AI hardware, such as neuromorphic computing, edge AI, and quantum-inspired architectures
  • Prepare detailed documentation of chip specifications, design methodologies, and test results for review and regulatory compliance

Skills / Requirements / Qualifications

  • Education: Bachelor’s or Master’s degree in Electrical Engineering, Computer Engineering, or a related field; Ph.D. preferred for advanced R&D roles
  • Technical Expertise: Strong background in digital circuit design, VLSI, and semiconductor architecture
  • AI/ML Knowledge: Understanding of deep learning algorithms, neural network structures, and their hardware implementation requirements
  • Programming Skills: Proficiency in hardware description languages (HDLs) such as Verilog, VHDL, or SystemVerilog, and scripting languages like Python or C++
  • EDA Tools: Experience with design and verification tools such as Cadence, Synopsys, or Mentor Graphics
  • Performance Optimization: Knowledge of parallel computing, GPU/TPU architectures, and hardware acceleration techniques
  • Problem-Solving: Strong analytical and debugging skills for identifying and resolving design inefficiencies
  • Collaboration: Ability to work in multidisciplinary teams bridging hardware, firmware, and AI software development

Job Zones

  • Title: Job Zone Five Extensive Preparation Needed
  • Education: Most of these occupations require graduate school. For example, they may require a master's degree, and some require a Ph.D., M.D., or J.D. (law degree).
  • Related Experience: Extensive skills, knowledge, and experience are needed for these occupations. Many require more than five years of experience. 
  • Job Training: Employees may need some on-the-job training, but most of these occupations assume that the person will already have the required skills, knowledge, work-related experience, or training.
  • Job Zone Examples: These occupations often involve coordinating, training, supervising, or managing the activities of others to accomplish goals. Very advanced communication and organizational skills are required. 
  • Specific Vocational Preparation in years: 4-7 years preparation (8.0 and above)

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