Founding Machine Learning Engineer – Time-Series & Health AI

MYDENTALWIG
Advanced Manufacturing. AI-Driven Healthcare. Preventive Innovation.

Building the future of intelligent manufacturing systems across healthcare, semiconductors, and AI infrastructure.

Location: Lancaster, California (On-site)
Employment Type: Full-Time
Compensation: $170,000–$230,000 annually, depending on experience, plus equity participation through the Company’s Equity Incentive Plan (Option Pool).

MYDENTALWIG is developing a new category of healthcare technology called Ingestion Intelligence™.

Our mission is to create the world’s first platform capable of detecting human ingestion behavior in real time and translating it into predictive metabolic intelligence using artificial intelligence, sensor technologies, and cloud computing.

By integrating hardware, machine learning, and metabolic science, we are building a platform designed to help people better understand how eating behaviors influence health before chronic disease develops.

As one of the Company’s earliest technical hires, you will help define the artificial intelligence architecture at the core of this platform.

We are seeking an exceptional Machine Learning Engineer with expertise in time-series analysis, predictive modeling, and applied artificial intelligence to lead development of the AI²™ Platform.

This is a founding engineering position responsible for designing, developing, and optimizing machine learning models capable of interpreting ingestion events, identifying behavioral patterns, and generating personalized metabolic predictions.

You will work closely with biomedical engineers, software developers, clinical advisors, and leadership to transform raw sensor data into meaningful, actionable intelligence.

This role offers the opportunity to solve complex scientific and engineering challenges while helping establish a new category of AI-enabled digital health technology.

• Design, develop, and deploy machine learning models for physiological and behavioral time-series data.
• Develop algorithms capable of identifying ingestion events and modeling individualized metabolic responses.
• Build predictive models that integrate data from DCI™, AI²™, Continuous Glucose Monitors (CGMs), and other health data sources.
• Design scalable data pipelines for training, validation, and continuous model improvement.
• Develop personalization algorithms using longitudinal behavioral and physiological data.
• Collaborate with software engineers to integrate machine learning models into cloud and mobile environments.
• Evaluate model performance using appropriate statistical and machine learning methodologies.
• Contribute to scientific publications, invention disclosures, and patent development where appropriate.
• Support future clinical validation studies through data analysis and model refinement.
• Stay current with advances in artificial intelligence, machine learning, and digital health technologies.

• Bachelor’s degree in Computer Science, Machine Learning, Artificial Intelligence, Biomedical Engineering, Data Science, or a closely related field.
• Five or more years of professional experience developing machine learning models in production environments.
• Strong experience with Python and machine learning frameworks such as PyTorch, Tensor Flow, or similar platforms.
• Solid understanding of supervised and unsupervised learning techniques.
• Experience working with complex time-series datasets.
• Strong analytical, mathematical, and statistical skills.
• Excellent communication and collaboration abilities.

• Master’s degree or Ph.D. in Machine Learning, Artificial Intelligence, Computer Science, Biomedical Engineering, or a related discipline.
• Experience applying machine learning to healthcare, biomedical signals, wearable technologies, digital therapeutics, or medical devices.
• Experience with physiological signal processing and multimodal sensor fusion.
• Knowledge of cloud-based machine learning infrastructure and MLOps.
• Familiarity with reinforcement learning, causal inference, or probabilistic modeling.
• Experience with edge AI and embedded machine learning.
• Publications in…