Closed listing
Applied AI/ML Scientist
- Vancouver, BC, Canada
$100,000 to $120,000 CAD / year · Full-time
This opening has closed.
The original listing is kept below for reference.
Job details
- Pay
- $100,000 to $120,000 CAD / year
- Job type
- Full-time
- Closing date
- Posting language
- English
- Posted
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Search current openingsOriginal job description
Archived listing. The details below describe a past opening.
Applied AI/ML Scientist / Engineer, Health Agents
About the role
We’re looking for an Applied AI/ML Scientist to help design, validate, and operationalize health agents. This role sits at the intersection of research and product engineering, with a strong focus on experimentation, multimodal modeling, personalization, evaluation, and real-world deployment. You will work on models that turn physiological and behavioral data into safe, structured outputs that power member experience.
- Responsibilities
- Design and evaluate models for on-device health agents across wearable, CGM, EHR, lab, and behavioral data
- Translate emerging research into practical implementations for personalized health inference under mobile and edge constraints
- Define feature pipelines, training data strategy, personalization logic, and structured output schemas for agent outputs
- Build experiments and evaluation frameworks for accuracy, safety, calibration, and member-level usefulness
- Work closely with product and engineering to move promising models into production mobile workflows, including on-device inference and privacy-preserving deployment
- Contribute to model and dataset selection for key domains such as sleep, stress, and metabolic health
- RequirementsStrong applied ML / data science experience in time-series, multimodal, or physiological data
- Experience designing experiments, evaluation frameworks, and model quality metrics
- Experience with agentic LLM systems
- Ability to work across research and engineering, not just modeling in isolation
- Nice to have:Experience with on-device / edge ML, quantization, or mobile deployment
- Familiarity with wearable, CGM, EHR, or lab data
- Experience integrating model outputs into product systems built on JavaScript / TypeScript platforms
More about this job
Responsibilities
Design, evaluate, and operationalize personalized health-agent models using wearable, CGM, EHR, laboratory, and behavioral data, including defining feature pipelines, training strategies, and structured outputs. Develop evaluation frameworks for accuracy, safety, calibration, and member usefulness, and collaborate with product and engineering teams to deploy models in privacy-preserving mobile and on-device workflows.
Requirements
Requires strong applied machine-learning or data-science experience with time-series, multimodal, or physiological data, along with experience designing experiments, evaluation frameworks, and model-quality metrics. Candidates should have experience with agentic LLM systems and be able to work across research and engineering; on-device ML, health data familiarity, and JavaScript or TypeScript product integration are advantageous.
Skills
- Applied Machine Learning
- Data Science
- Time-Series Modeling
- Multimodal Modeling
- Physiological Data Analysis
- Experiment Design
- Model Evaluation
- Agentic LLM Systems
- Personalization
- Feature Pipelines
- Training Data Strategy
- Model Calibration
- On-Device Inference
- Edge Machine Learning
- Quantization
- Mobile Deployment
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Healthcare
- Science & Research
- Data & Analytics
- Software
Keywords
- Applied AI
- Machine Learning
- Data Science
- Health Agents
- Wearable Data
- Continuous Glucose Monitoring
- Electronic Health Records
- Laboratory Data
- Behavioral Data
- Time-Series Data
- Multimodal Modeling
- Physiological Data
- Personalized Health Inference
- Agentic LLM Systems
- Feature Pipelines
- Training Data Strategy
- Structured Output Schemas
- Model Evaluation
- Accuracy
- Safety
- Calibration
- On-Device Inference
- Edge Machine Learning
- Mobile Deployment
- Privacy-Preserving Deployment
- Quantization
- Sleep
- Stress
- Metabolic Health
- JavaScript