AI/ML Engineer
- Vancouver, BC, Canada
Salary: $110K – $120K · Full-time
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Job details
- Pay
- Salary: $110K – $120K
- Job type
- Full-time
- Apply by
- Experience
- Note: Candidates need experience in the biomedical domain, and at least 6 years of work experience
- Posting language
- English
- Posted
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.
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
Not detected in the job text
Categories
- Technology
- Healthcare
- Data & Analytics
- Software
- Science & Research
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