NeuraVia — Chief Technology Officer (unpaid until revenue is produced, Remote)
Help build a novel multimodal AI system for Alzheimer’s progression modeling.
About NeuraVia
NeuraVia is a neurotechnology startup developing an advanced deep learning framework that integrates demographic data, adaptive cognitive test results, and adaptive voice test features to accurately predict Alzheimer’s disease progression. Our team is composed of motivated researchers and engineers focused on applying cutting-edge AI to real-world neurological care.
Role: Chief Technology Officer
Type: Unpaid until revenue is being generated
Time Commitment: 30+ hours per week
Location: Remote
What You’ll Work On
- Develop large amounts of realistic synthetic data (including audio and csv data) via generative models from pre-existing data samples
- Refine and improve the model architectures for three modalities: cognitive test embeddings, voice test embeddings, and demographic data.
- 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
- Prepare and train these model architectures on vast amounts of realistic synthetic data
- Collaborate with the engineering and research teams to containerize experiments and prepare inference-ready MVP modules.
Requirements
- Must be actively enrolled in a bachelor's degree in computer science (AI/ML) or currently enrolled into an accredited post-secondary university/college’s bachelors of science program for Computer Science (AI/ML)
- Proficiency in Python and PyTorch.
- Familiarity with generative modeling, including flow-based models, GANs, retrieval-augmented generation (RAG), and large language models (LLMs).
- Understanding of probabilistic modeling, diffusion models, Bayesian methods, and dimensionality reduction techniques
- Comprehensive understanding of sequence models (LSTM/GRU), Transformers, and VAEs.
- Experience with graph neural networks (GAT/R-GAT) and multimodal fusion methods.
- Proficient in cloud computing concepts with experience using AWS
- Comfortable reading and implementing academic research papers.
- Strong debugging, version control (Git/GitHub), and documentation skills.
- Availability of 30+ hours per week with consistent progress updates.
Preferred
- Prior experience working on multimodal or neuro-related ML projects.
- Exposure to MLOps practices (Docker, continuous monitoring, continuous training, experiment tracking, etc.).
Benefits
- Collaborate on a real-world neuro-AI project with technical depth and publication potential.
- Gain more experience in full-stack ML research from data generation to architecture design
- Eligible for salary and equity compensation upon revenue generation.
- Participates in strategic decision-making as part of the executive team.
How to Apply
Interested candidates can apply via LinkedIn Quick
Applications are reviewed on a rolling basis. Please include your GitHub, portfolio, 1–2 examples of relevant ML work or projects, and resume.
