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TEEMA

AI/ML Engineer

  • Vancouver, BC, Canada

$110,000 to $120,000 CAD / year · Full-time

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The original listing is kept below for reference.

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Job details

Pay
$110,000 to $120,000 CAD / year
Job type
Full-time
Closing date
Posting language
English
Posted

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Original 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.
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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 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

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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

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