Summer 2026 Intern - Quantum Computing or Embedded AI

Western Digital•San Jose, CA

17h

About The Position

This internship opportunity is in Western Digital's Research organization, which tackles advanced long-horizon problems related to data storage, computation and communication. In addition to supporting magnetic storage and memory roadmap of Western Digital, we are focused on several key areas that are well aligned with our existing experimental and theoretical skills, and existing laboratory facilities Western Digital Research has a number of projects for ambitious graduate students. Our group has projects emphasizing either embedded AI inference or the design and testing of superconducting quantum computing qubits. Depending on the skills of the student, the responsibilities may be divided into one of two tracks. Track 1: As an Intern in Quantum Computing, your project will require several of the following tasks: Assist in the design and testing of superconducting quantum computing qubits Collaborate with team members on quantum computing experiments and simulations Propose and simulate laboratory test structures for evaluating the performance of our qubit designs Analyze and interpret experimental data using various data analysis and visualization tools Participate in the development of quantum information theory concepts and applications Assist in the preparation of research reports and presentations Engage in literature reviews to stay updated on the latest advancements in quantum computing Support the team in maintaining and calibrating quantum computing equipment Collaborate with interdisciplinary teams to integrate quantum computing solutions with other technologies Participate in regular team meetings and contribute ideas for project improvements Assist in documenting research findings and maintaining accurate records of experiments Contribute to the development of quantum error correction techniques Track 2: As an intern in Embedded AI Inference, your project will require several of the following tasks: Assist in the fine-tuning and optimization of AI models for embedded systems Implement and test machine learning algorithms on various hardware platform Create error models to simulate the impact of noise or circuit variability in analog inference accelerators Contribute to the design of efficient neural network architectures for resource-constrained devices Collaborate with the team to improve inference speed and accuracy on embedded devices Participate in data collection, preprocessing, and augmentation for AI model training Assist in the evaluation and benchmarking of AI models on different embedded platforms Contribute to the development of firmware tools and frameworks for embedded AI deployment Support the integration of AI models with embedded software and hardware systems Engage in literature reviews to stay updated on the latest advancements in embedded AI Assist in the preparation of technical documentation and research reports In either track, you will be part of a broader collaborative team, which places strong emphasis on communication skills and team spirit, in addition to strong technical skills.

Requirements

Currently pursuing a PhD degree in Physics, Computer Science, Electrical Engineering, or a related field
Excellent problem-solving skills and ability to think critically
Experience with data analysis and visualization tools
Strong attention to detail and ability to work efficiently in a team-focused environment
Excellent organizational skills and ability to manage multiple tasks simultaneously
Effective communication skills, both written and verbal
Ability to work collaboratively in a team environment
Proficiency in version control systems (e.g., Git)
Familiarity with agile development methodologies
Demonstrated ability to learn and adapt to new technologies quickly
Strong foundation in quantum computing principles and quantum mechanics
Proficiency in programming languages such as Python, Q#, or Qiskit
Solid understanding of linear algebra and its applications in quantum computing
Basic understanding of quantum information theory
Strong background in machine learning and deep learning algorithms
Proficiency in programming languages such as Python, C++, or TensorFlow
Experience with embedded systems and microcontrollers
Familiarity with AI model optimization techniques for resource-constrained devices
Knowledge of computer architecture, digital logic design and and hardware-software co-design
Experience with signal processing and digital signal processors (DSPs)
Understanding of low-power design principles for embedded systems
Familiarity with AI frameworks such as TensorFlow Lite, ONNX Runtime, or LLVM MLIR

Nice To Haves

Academic projects, coursework or other experience in quantum computing, RF circuit design, or other superconducting device physics is preferred
Familiarity with quantum error correction techniques
Experience with quantum circuit design and optimization
Some experience with FPGA programming is preferred

Responsibilities

Assist in the design and testing of superconducting quantum computing qubits
Collaborate with team members on quantum computing experiments and simulations
Propose and simulate laboratory test structures for evaluating the performance of our qubit designs
Analyze and interpret experimental data using various data analysis and visualization tools
Participate in the development of quantum information theory concepts and applications
Assist in the preparation of research reports and presentations
Engage in literature reviews to stay updated on the latest advancements in quantum computing
Support the team in maintaining and calibrating quantum computing equipment
Collaborate with interdisciplinary teams to integrate quantum computing solutions with other technologies
Participate in regular team meetings and contribute ideas for project improvements
Assist in documenting research findings and maintaining accurate records of experiments
Contribute to the development of quantum error correction techniques
Assist in the fine-tuning and optimization of AI models for embedded systems
Implement and test machine learning algorithms on various hardware platform
Create error models to simulate the impact of noise or circuit variability in analog inference accelerators
Contribute to the design of efficient neural network architectures for resource-constrained devices
Collaborate with the team to improve inference speed and accuracy on embedded devices
Participate in data collection, preprocessing, and augmentation for AI model training
Assist in the evaluation and benchmarking of AI models on different embedded platforms
Contribute to the development of firmware tools and frameworks for embedded AI deployment
Support the integration of AI models with embedded software and hardware systems
Engage in literature reviews to stay updated on the latest advancements in embedded AI
Assist in the preparation of technical documentation and research reports

Benefits

paid vacation time
paid sick leave
medical/dental/vision insurance
life, accident and disability insurance
tax-advantaged flexible spending and health savings accounts
employee assistance program
other voluntary benefit programs such as supplemental life and AD&D, legal plan, pet insurance, critical illness, accident and hospital indemnity
tuition reimbursement
transit
the Applause Program
employee stock purchase plan
the Western Digital Savings 401(k) Plan