Closed listing
AI/ML Engineer
- Vancouver, BC, Canada
$110,000 to $120,000 CAD / year · Full-time
This opening has closed.
The original listing is kept below for reference.
Job details
- Pay
- $110,000 to $120,000 CAD / year
- Job type
- Full-time
- Closing date
- Posting language
- English
- Posted
Similar jobs hiring now
Search current openingsOriginal job description
Archived listing. The details below describe a past opening.
Applied AI/ML Scientist - Interviews are onsite in Vancouver
Location: Onsite
Salary: $110K – $120K
Location: This is onsite 5 days a week in downtown Vancouver.
About the Role
We’re looking for an Applied AI/ML Scientist to help design, validate, and operationalize Mimik’s 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 Mimik’s member experience.
Key Responsibilities
- Model Design: Design and evaluate models for Mimik’s on-device health agents across wearable, CGM, EHR, lab, and behavioral data.
- Translation: Translate emerging research into practical implementations for personalized health inference under mobile and edge constraints.
- Architecture: Define feature pipelines, training data strategy, personalization logic, and structured output schemas for agent outputs.
- Evaluation: Build experiments and evaluation frameworks for accuracy, safety, calibration, and member-level usefulness.
- Engineering Collaboration: Work closely with product and engineering to move promising models into production mobile workflows, including on-device inference and privacy-preserving deployment.
- Domain Focus: Contribute to model and dataset selection for key domains such as sleep, stress, and metabolic health.
Requirements
- Experience: Strong applied ML / data science experience in time-series, multimodal, or physiological data.
- Frameworks: Experience designing experiments, evaluation frameworks, and model quality metrics.
- Technology: Experience with agentic LLM systems.
- Mindset: 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 models using wearable, CGM, EHR, laboratory, and behavioral data, with a focus on domains such as sleep, stress, and metabolic health. Build evaluation frameworks and collaborate with product and engineering teams to deploy safe, useful models in mobile workflows, including privacy-preserving on-device inference.
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 or edge 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
- Model Quality Metrics
- Agentic LLM Systems
- Personalization
- Feature Pipelines
- Training Data Strategy
- On-Device Inference
- Edge Machine Learning
- Quantization
- Mobile Deployment
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Healthcare
- Data & Analytics
- Software
- Science & Research
Keywords
- Applied AI
- Machine Learning
- Health Agents
- Wearable Data
- Continuous Glucose Monitoring
- Electronic Health Records
- Laboratory Data
- Behavioral Data
- Time-Series Data
- Multimodal Modeling
- Personalized Health Inference
- Agentic LLM Systems
- Feature Pipelines
- Training Data Strategy
- Structured Output Schemas
- Model Evaluation
- Accuracy
- Safety
- Calibration
- Member-Level Usefulness
- On-Device Inference
- Privacy-Preserving Deployment
- Edge Machine Learning
- Quantization
- Mobile Deployment
- JavaScript
- TypeScript
- Sleep
- Stress
- Metabolic Health