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Within 25 km of Coquitlam

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TEEMA

AI/ML Engineer

Vancouver, BC, Canada

Salary: $110K – $120K · Full-time

Within 25 km of Coquitlam · About 24.4 km from the city centre

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Job details

Pay: Salary: $110K – $120K
Job type: Full-time
Apply by: Dec 4, 2026
Experience: Note: Candidates need experience in the biomedical domain, and at least 6 years of work experience
Posted: Nov 4, 2026

Original job description

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.

Note: Candidates need experience in the biomedical domain, and at least 6 years of work experience

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.

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More about this job

Responsibilities

Design, evaluate, and operationalize personalized health models using wearable, CGM, EHR, laboratory, and behavioral data, with attention to safety, accuracy, and usefulness. Develop model architectures and evaluation frameworks, and collaborate with product and engineering teams to deploy models in privacy-preserving mobile and edge workflows.

Requirements

Requires at least six years of work experience, including biomedical-domain experience and strong applied machine learning or data science experience with time-series, multimodal, or physiological data. Candidates should have experience designing experiments and evaluation frameworks, working with agentic LLM systems, and collaborating across research and engineering.

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

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Keywords

Applied AI
Machine Learning
Biomedical
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
Member-Level Usefulness
On-Device Inference
Privacy-Preserving Deployment
Edge Machine Learning
Quantization
Mobile Deployment
JavaScript
TypeScript
Sleep

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