Cahira Technologies – Revolutionizing Neuro-Intervention
Are you an exceptional AI/ML Engineer with a passion for transforming human capabilities and treating neurological diseases? Join Cahira Technologies, a groundbreaking neurotech startup poised to redefine personalized brain intervention. We're building the future of adaptive neuromodulation, leveraging vast neuroscientific datasets to develop intelligent systems that optimize cognitive function and precisely target disease states.
We are seeking AI/Machine Learning Engineers who will architect and implement the core AI pipelines that drive our innovative solutions. This is a unique opportunity to build the future of Neuro-AI and make a transformative impact on human health and performance.
What you'll do:
Design, develop, and deploy scalable ML pipelines for ingesting, cleaning, and processing diverse multimodal neuroscience data (EEG, fMRI, LFP, single-spike, behavioral, clinical, blood test data).
Lead the research, development, and training of advanced AI models (Deep Learning, Reinforcement Learning) to:
Predict optimal brain states for cognitive enhancement (e.g., learning, memory, attention).
Identify and predict pathological brain patterns associated with neurological disorders (e.g., seizure, Parkinson’s, Alzheimer’s, brain cancer, chronic pain).
Generate personalized, adaptive neuromodulation parameters for both enhancement and disease treatment.
Drive the selection of appropriate AI/ML frameworks, tools, and cloud infrastructure.
Collaborate closely with our Computational Neuroscientist to ensure scientific rigor and clinical relevance of our AI models.
Contribute to our intellectual property and scientific publications.
Required Skills & Experience:
PhD or Master's degree in Computer Science, Electrical Engineering, or a related quantitative field.
5+ years of experience in AI/Machine Learning engineering, with a strong portfolio of successfully deployed ML models.
Expertise in Deep Learning architectures, especially for time-series data (RNNs, LSTMs, Transformers, CNNs).
Proficiency in Python and leading ML frameworks (e.g., PyTorch, TensorFlow).
Demonstrated experience with multimodal data fusion and complex data preprocessing.
Solid understanding of MLOps principles for building robust and scalable ML systems.
Experience with cloud computing platforms (AWS, GCP, Azure) and their AI/ML services.
Preferred Skills & Experience:
Experience with Reinforcement Learning (RL) for control systems or optimization.
Familiarity with neuroscientific data (EEG, fMRI, LFP, spikes) and common neuroimaging/electrophysiology analysis toolboxes.
Experience with Explainable AI (XAI) techniques for medical/neuroscience applications.
Experience with Edge-AI
Experience with foundation models
Background in signal processing for biological signals.
Skills Required
- PhD or Master's degree in Computer Science, Electrical Engineering, or related quantitative field
- 5+ years of experience in AI/Machine Learning engineering with deployed ML models
- Expertise in deep learning architectures for time-series (RNNs, LSTMs, Transformers, CNNs)
- Proficiency in Python
- Proficiency with ML frameworks (PyTorch, TensorFlow)
- Demonstrated experience with multimodal data fusion and complex data preprocessing
- Solid understanding of MLOps principles for robust, scalable ML systems
- Experience with cloud computing platforms and AI/ML services (AWS, GCP, Azure)
- Experience with Reinforcement Learning (RL) for control systems or optimization
- Familiarity with neuroscientific data (EEG, fMRI, LFP, spikes) and neuroimaging/electrophysiology toolboxes
- Experience with Explainable AI (XAI) techniques for medical/neuroscience applications
- Experience with Edge-AI
- Experience with foundation models
- Background in signal processing for biological signals
