Type: Unpaid volunteer internship (remote, must be performed from within the United States)

Overview:

Join a frontier R&D initiative exploring hybrid quantum-classical AI systems. You’ll work on real code, real models, and real technical documentation. You’ll collaborate directly with a Classical AI Intern to build, test, and analyze quantum-enhanced approaches to machine learning.

Learning Outcomes:

• Quantum SDKs like Qiskit, PennyLane, or TKET
• Variational quantum circuits (VQCs), quantum kernels, and hybrid training loops
• Simulating noise and applying mitigation techniques
• Collaborating across domains on interpretable AI models

What You’ll Gain:

This is a rigorous volunteer internship designed to help you grow as a researcher and technologist. You’ll build real hybrid AI systems alongside an experienced founder-led team, strengthen your technical portfolio, and gain exposure to high-impact domains like sustainable technology, transparent AI, and quantum computing. Outcomes may include:

• Featured contributions in open-source notebooks or prototype repositories
• Optional co-authorship of whitepapers or preprints (e.g., arXiv, SSRN)
• Technical reference or mentorship from project leadership
• Hands-on collaboration experience across AI and quantum teams
• Demonstrable portfolio artifacts suitable for academic or industry applications

Required Skills:

• Familiarity with qubits, gates, measurement, and entanglement
• Experience using a quantum SDK (Qiskit, PennyLane, TKET, or Cirq)
• Basic neural network experience using PyTorch, TensorFlow, or Keras
• Python proficiency and use of GitHub/Jupyter workflows

Collaboration:

You’ll work closely with a Classical AI Intern to compare quantum and classical models, co-develop hybrid pipelines, and support interpretability analysis.

How to Apply:

Submit your interest via our contact form. Please include any GitHub, portfolio, or profile links. No résumé required at this time.

Type: Unpaid volunteer internship (remote, must be performed from within the United States)

Overview:

This position focuses on designing, training, and interpreting deep learning models in collaboration with a Quantum AI Intern. You’ll explore model transparency, hybrid systems, and sustainability-focused AI applications through hands-on work.

Learning Outcomes:

• Building and tuning neural networks (NLP, CNN, transformer)
• Model interpretability using SHAP, LIME, custom layer, and attention mechanisms
• Working with real datasets in sustainability and cybersecurity contexts
• Collaborating on hybrid quantum-classical modeling experiments

What You’ll Gain:

This is a rigorous volunteer internship designed to help you grow as a researcher and technologist. You’ll build real hybrid AI systems alongside an experienced founder-led team, strengthen your technical portfolio, and gain exposure to high-impact domains like sustainable technology, transparent AI, and quantum computing. Outcomes may include:

• Featured contributions in open-source notebooks or prototype repositories
• Optional co-authorship of whitepapers or preprints (e.g., arXiv, SSRN)
• Technical reference or mentorship from project leadership
• Hands-on collaboration experience across AI and quantum teams
• Demonstrable portfolio artifacts suitable for academic or industry applications

Required Skills:

• Strong Python programming and use of PyTorch or TensorFlow
• Ability to implement, train, and debug neural networks
• Understanding of loss functions, optimizers, and evaluation metrics
• Use of GitHub, Jupyter, or notebook-based development workflows

Collaboration:

You’ll work closely with a Quantum AI Intern to build hybrid pipelines, benchmark performance, and contribute to a shared knowledge base.

How to Apply:

Submit your interest via our contact form. Please include any GitHub, portfolio, or profile links. No résumé required at this time.