Overview
Read the full description before applying.
MUST HAVE: Hands-on experience implementing quantum computing algorithms, quantum machine learning methods, or hybrid quantum-classical workflows using frameworks such as Qiskit, PennyLane, Cirq, or equivalent.
At M31 Biomedical AI, we are developing foundation models and advanced computational approaches for understanding human health and biology across medical imaging, clinical data, and other biomedical modalities. We are expanding our research into quantum computing and quantum machine learning, with a particular interest in understanding where quantum and hybrid quantum-classical methods could meaningfully address computational problems in biomedical AI, healthcare, and scientific discovery.
We’re seeking an exceptional Quantum Computing Research Intern to work alongside our AI researchers and research collaborators to explore, implement, benchmark, and evaluate quantum approaches for clinically and scientifically meaningful problems.
This is not intended to be a purely theoretical role. We are looking for someone who can translate quantum computing concepts into working experiments, critically evaluate whether quantum approaches provide meaningful value, and build reproducible research pipelines.
What You’ll Do
• Implement and benchmark. Build quantum and hybrid quantum–classical methods — variational quantum algorithms, quantum kernels, quantum neural networks, and emerging quantum ML approaches — and evaluate them against strong classical and deep learning baselines.
• Run experiments. Design and run experiments on quantum simulators and, where appropriate, real quantum hardware.
• Find the real applications. Explore where quantum computing could apply to biomedical AI — medical imaging, clinical and biological data, optimization, representation learning, and predictive modelling — working with AI scientists, ML engineers, clinicians, and external collaborators to identify problems where quantum methods may add genuine value.
• Characterise the limits. Investigate noise, scalability, circuit depth, data encoding, computational cost, and hardware constraints.
• Keep the work reproducible. Document and maintain reproducible workflows using Python, Git, and cloud-based research tools; review, debug, and improve research code.
• Read, write, and explain. Conduct technical literature reviews, contribute to publications, technical reports and internal proposals, and communicate findings clearly to both technical and interdisciplinary audiences.
• Work with AI scientists, machine learning engineers, clinicians, and external research collaborators to identify problems where quantum approaches may provide meaningful scientific or computational value
• Contribute to research publications, technical reports, internal research proposals, and presentations
• Clearly communicate complex quantum computing concepts and experimental findings to both technical and interdisciplinary audiences
Required Skills & Background
- Currently pursuing or recently completed an undergraduate, master’s, or PhD degree in Computer Science, Physics, Mathematics, Engineering, Quantum Information, Computational Science, or a related field
- Strong programming skills in Python
- Hands-on experience with at least one quantum computing framework such as Qiskit, PennyLane, Cirq, CUDA-Q, or equivalent
- Strong understanding of fundamental quantum computing concepts, including:
qubits and quantum states; quantum gates and circuits; measurement; entanglement
- variational quantum algorithms
- noise and limitations of current quantum hardware
- Hamiltonians and expectation values
- Understanding of machine learning fundamentals and experimental evaluation
- Ability to independently read and implement methods from technical research papers
- Strong mathematical foundation, particularly linear algebra, probability, optimization, and statistics
- Strong problem-solving, communication, and teamwork skills
Nice-to-Have
- Experience running experiments on real quantum hardware, particularly IBM Quantum environments
- Experience with quantum machine learning - quantum kernels, variational quantum classifiers, quantum neural networks, or quantum-enhanced optimization
- Experience with PyTorch, JAX, TensorFlow, or other machine learning frameworks
- Experience with deep learning architectures including Transformers or foundation models
- Experience with biomedical datasets, medical imaging, EHR, genomics, single-cell data, or computational biology
· Experience formulating problem Hamiltonians — molecular Hamiltonians for VQE or quantum chemistry, or optimisation problems encoded as Ising/QUBO models for QAOA or quantum annealing
· Experience with graph-structured problems, such as QAOA on graph instances, or graph representations of molecules and biological networks
- Knowledge of quantum error mitigation, circuit optimization, or hardware-aware algorithm design
- Research publications, preprints, open-source contributions, or substantial research projects in quantum computing, quantum ML, machine learning, or computational science
- Experience with agentic coding tools such as Claude Code or Codex
What We’re Looking For
We are particularly interested in candidates who are technically rigorous and scientifically skeptical.
You should be excited about quantum computing while also being willing to demonstrate when a classical approach is better. We value candidates who can formulate strong experiments, establish appropriate baselines, identify limitations, and distinguish genuine computational advances from interesting demonstrations.
The strongest candidates will have evidence that they have actually built and tested quantum systems or algorithms, rather than simply completed coursework in quantum computing.
Why Join Us
- Help establish a new quantum computing research direction within a biomedical AI organization, at the intersection of quantum computing, artificial intelligence, and medicine
- Explore high-impact research problems using real biomedical and clinical use cases
- Collaborate with AI researchers, clinicians, computational scientists, and external research partners
- Gain exposure to emerging quantum computing technologies and hybrid quantum-classical research
- Contribute to publications and potentially new research directions in quantum biomedical AI
- Work in a mission-driven environment focused on translating advanced computation into meaningful healthcare applications
Application Requirements
- Resume/CV
- Brief cover letter describing your quantum computing experience and why you are interested in applying quantum methods to biomedical AI
- GitHub, research portfolio, publications, or examples of quantum computing projects strongly encouraged
About M31
M31 Biomedical AI is a biomedical imaging company developing foundation models for medical image segmentation and analysis. Our technology enables universal understanding of medical images across modalities and institutions. We’re now collaborating with leading research partners to extend this vision beyond imaging to include multi-modal clinical data, in order to advance patient healthcare, understand complex diseases and improve therapeutic discovery.
Job Type: Full-time (12-month renewable contract)
Location: Hybrid remote – Toronto, ON (M5S 1A8)
Compensation: CA$28 – 32/hour, based on experience
Benefits:
- Flexible schedule
- Work-from-home option
- Mentorship and publication opportunities
