Full-Time

Quantum Error Correction Research Scientist Intern

Fall 2026

NVIDIA

NVIDIA

10,001+ employees

Designs GPUs and AI HPC platforms

Compensation Overview

$20 - $71/hr

Company Historically Provides H1B Sponsorship

Remote in USA + 1 more

More locations: Remote in Canada

Remote

Category
Quantum Computing
Required Skills
Python
Machine Learning
Requirements
  • Pursuing a Master's, or PhD in Physics, Electrical/Computer Engineering, Computer Science, or a related field.
  • Background in quantum computing fundamentals, especially the fundamentals of quantum error correction, stabilizer codes, coding theory, and decoders.
  • Solid proficiency in Python or similar scientific programming language experience.
  • Good teamwork, communication, and documentation skills.
Responsibilities
  • Developing automated quantum error correcting code discovery protocols and pipelines.
  • Using AI and automation to identify novel quantum error correcting codes that achieve high-performance across encoding rate, error suppression, and system realizability.
  • Integrating various classical machine learning methods for identifying high-performance code constructions, building bespoke machine learning models tailored to high-performance code design, and developing machine learning-based decoders for topological and/or QLDPC codes, with an emphasis on improving decoding accuracy, scalability, and adaptability to quantum logic settings.
  • Building out methods to identify and evaluate high-performance error correcting codes and decoders that directly incorporate hardware constraints of various types of physical systems
  • Analyzing and evaluating the performance of fault-tolerant circuits implementing quantum error correcting logic and memory using analysis and simulation tools.
Desired Qualifications
  • Hands-on experience with tensor network, state vector, or quantum error correction methods for quantum circuit simulation.
  • Experience with neural network-based quantum error correction decoders, either for topological or QLDPC codes.
  • Familiarity with qubit loss, leakage, and other non-Pauli noise mechanisms, including methods for incorporating this information into QEC code design and decoder design.
  • Experience with magic state preparation, distillation, or factory design, including decoding and performance analysis of magic state factories.
  • GPU-accelerated simulation experience with cuQuantum, CUDA-Q, or related NVIDIA quantum software.

NVIDIA designs and manufactures graphics processing units (GPUs) and computing platforms used for gaming, data centers, and artificial intelligence. These products work by using parallel processing to handle complex mathematical calculations much faster than standard computer processors, supported by a software ecosystem that allows developers to build and run AI models. Unlike competitors that may focus solely on hardware, NVIDIA integrates its chips with specialized software and cloud services to create a complete environment for high-performance tasks. The company’s goal is to provide the underlying technology necessary to power advanced computing, from realistic video game graphics to autonomous vehicles and large-scale data analysis.

Company Size

10,001+

Company Stage

IPO

Headquarters

Santa Clara, California

Founded

1993

Simplify Jobs

Simplify's Take

What believers are saying

  • Over 80% AI-training GPU share supports pricing power and scale.
  • Microsoft partnership deepens Windows and Azure distribution for agentic AI.
  • Helix and Abridge expand NVIDIA into infrastructure and regulated healthcare.

What critics are saying

  • U.S. export controls restrict H200 sales to China case-by-case.
  • Blackwell shortages leave demand unfilled and invite accelerator competitors.
  • Helix exposes NVIDIA to capital-intensive projects and partner execution risk.

What makes NVIDIA unique

  • CUDA and full-stack software lock developers into NVIDIA's ecosystem.
  • Blackwell GPUs target AI factories, not just gaming or graphics.
  • Its platforms span gaming, data centers, robotics, automotive, and healthcare.

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