Job Description

NeuraVia Operations & Management Intern (Unpaid, Remote)

Role

Machine Learning Research Volunteer

Type

Unpaid (volunteer research Volunteership)

Time Commitment

15 hours per week

Location

Remote

What Youll Work On

Develop and refine model architectures for three modalities: cognitive test embeddings, voice test embeddings, and vascular biomarkers.

Implement modules including:

• Learned encoders and latent-space disentanglement
• R-GAT graph encoders for cognitive subdomains
• HuBERT-based audio embedding pipelines
• Custom latent fusion VAEs using product-of-experts rules
• Multimodal diffusion-transformer and LSTM hybrid networks for time-series trajectory prediction
• Bayesian heads for uncertainty estimation

Collaborate with the engineering and research teams to containerize experiments and prepare inference-ready MVP modules.

Contribute to documentation, reproducibility, and validation of models on synthetic and real datasets.

Requirements

• Proficiency in Python and PyTorch.
• Must be incoming or currently enrolled in a Bachelors/Associates (or any equivalent) Program at a licensed post-secondary institution.
• Experience implementing sequence models (LSTM/GRU), Transformers, and VAEs.
• Familiarity with graph neural networks (GAT/R-GAT) and multimodal fusion methods.
• Comfortable reading and implementing from academic research papers.
• Strong debugging, version control (Git/GitHub), and documentation skills.
• Availability of 15 hours per week with consistent progress updates.

Preferred

• Prior experience working on multimodal or neuro-related ML projects.
• Understanding of probabilistic modeling, diffusion models, or Bayesian methods.
• Exposure to MLOps practices (Docker, experiment tracking, etc.).

Benefits

• Collaborate on a real-world neuro-AI project with technical depth and publication potential.
• Gain experience in full-stack ML research from architecture design to deployment.
• Earn a detailed technical reference and recognition upon successful completion.

How to Apply

Interested candidates can apply via Linkedin Quick.

Applications are reviewed on a rolling basis. Please include your GitHub, portfolio, and 12 examples of relevant ML work or projects.

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