Senior AI Engineer EEG Cognitive Scoring Systems

Senior AI Engineer – EEG Cognitive Scoring Systems

Job Description 1: Senior AI Engineer – EEG Cognitive Scoring Systems

Location: Delhi

Employment Type: Full-Time

Experience: 4+ Years

Start Date: Immediate Joiners Preferred

About the Role

We are looking for an experienced Senior AI Engineer to build and improve production machine learning systems that transform EEG signals into cognitive and stress-state scores.

This role is intended for engineers who have personally built, deployed, and maintained production AI systems. Candidates whose experience is limited to academic research, internships, proof-of-concept projects, or model training without production deployment are unlikely to be a good fit.

Strong experience in machine learning for time-series or sequential data is essential. Experience with EEG, biosignals, wearable sensors, or healthcare AI is highly preferred but not mandatory. Engineers with strong production experience in other time-series domains are encouraged to apply.

This is NOT a Generative AI / LLM Engineering role. Candidates whose experience is primarily focused on prompt engineering, chatbots, RAG pipelines, agentic AI, or LLM applications without substantial machine learning and time-series modeling experience are unlikely to be a good fit.

You will work closely with neuroscientists, biomarker researchers, and AI engineers to improve model accuracy, robustness, explainability, and production readiness for real-world cognitive assessment systems.

Key Responsibilities

• Design, build, and optimize machine learning models for EEG-based cognitive scoring.
• Develop robust feature engineering pipelines for time-series and physiological signals.
• Design models that generalize well to noisy real-world data.
• Translate validated biomarker research into scalable production AI systems.
• Build and optimize production ML pipelines for training, inference, monitoring, and continuous improvement.
• Evaluate models using rigorous statistical methods and scientific validation.
• Improve model explainability and support evidence-based engineering decisions.
• Collaborate closely with neuroscientists, AI researchers, and software engineers.
• Document experiments, technical decisions, and production improvements.

Required Qualifications

• 4+ years of hands-on experience building, deploying, and maintaining production machine learning systems.
• Demonstrated ownership of AI systems from development through deployment, monitoring, and iterative improvement.
• Research-only experience is not sufficient.
• Strong experience developing machine learning models for time-series or sequential data.
• Excellent understanding of feature engineering, model validation, statistical analysis, and model evaluation.
• Strong Python programming skills.
• Hands-on experience with PyTorch, TensorFlow, or Scikit-learn.

Preferred Qualifications

• EEG signal processing.
• Physiological signals (EEG, ECG, PPG, EMG, wearable sensors).
• Time-series deep learning (LSTM, GRU, TCN, Transformers, Temporal CNNs, or similar architectures).
• Multimodal machine learning.
• Explainable AI.
• Scientific computing.
• Experience collaborating with multidisciplinary engineering and research teams.

Technologies You'll Work With

• Python
• PyTorch
• TensorFlow
• Scikit-learn
• MNE
• NumPy
• Pandas
• Git
• Signal Processing
• Time-Series Machine Learning

Why Join Brainwave Science

• Build AI systems that solve real-world problems in neuroscience and healthcare.
• Work on challenging machine learning problems involving cognitive intelligence and biosignals.
• Collaborate with experienced AI engineers, neuroscientists, and biomarker researchers.
• Own production of AI systems from research to deployment.
• Be part of a fast-moving engineering culture focused on technical excellence, innovation, and meaningful impact.

Skills Required
Git, Pandas, Tensorflow, Numpy, Pytorch, Signal Processing, Python