Senior AI Applied Scientist II – Vancouver, British Columbia | Visa & Salary Insights

📍 Location: ca

🏷 Type: Full-time

Job Intelligence

📍 More in this location:
Browse AI jobs in ca

🏷 Similar roles:
View similar Full-time jobs

🌐 Explore all jobs:
View all AI job listings

Job Overview

The Senior AI Applied Scientist II role at Prenuvo focuses on building next-generation AI systems for whole-body MRI and proactive healthcare. This position sits within a research-driven organization transitioning from task-specific models to foundation models and multimodal learning systems. The ideal candidate is a highly independent ML scientist capable of owning an entire model domain end-to-end, from research framing and experimental design to production deployment and clinical validation. You will work across imaging, clinical data, and large-scale representation learning to improve early disease detection and patient outcomes globally.

📅 Job Timeline & Status

This role is currently in an active hiring phase with no visible deadline, suggesting rolling review until sufficient candidate volume is reached. Given the seniority and niche ML + medical imaging scope, applicants with strong foundation model or medical AI experience should apply immediately due to expected high competition and limited headcount.

🌍 Work Eligibility & Location

This is a Hybrid Vancouver role requiring in-person collaboration with clinical and ML teams. The position is not open to visa sponsorship, making it primarily accessible to candidates already eligible to work in Canada. Cross-functional interaction with clinical operations and engineering teams is a core part of the role.

💰 Salary Intelligence

The compensation is competitive for a Canadian AI research role but below top-tier US AI lab benchmarks. However, the role compensates with high-impact healthcare applications, strong research ownership, and access to real clinical data pipelines, making it particularly valuable for long-term AI research career growth.

📊 Role Breakdown

This role centers on designing and scaling foundation models for multimodal medical imaging and clinical data fusion. You will independently own a full ML domain, including dataset design, architecture selection, training strategies, evaluation protocols, and deployment validation. Expect to work with self-supervised learning, including masked image modeling, contrastive objectives, and JEPA-style architectures for representation learning on large MRI datasets. A significant portion of the work involves integrating imaging data with structured clinical records and longitudinal patient information. You will also refine annotation pipelines, define labeling standards, and ensure data quality aligns with clinical relevance. On the modeling side, you will leverage Vision Transformers, multimodal transformers, and generative AI techniques to build scalable systems. Approximately 40% of time is spent on experimentation, 30% on data/clinical alignment, 20% on deployment readiness, and 10% on cross-team research strategy and mentoring.

🧩 Required Skills & Fit

📈 Difficulty & Competitiveness

This is a highly competitive senior research role requiring deep expertise in both ML theory and production-level medical AI systems. Candidates without prior experience in multimodal learning or clinical datasets may struggle to pass technical screening.

🚀 Career Impact

This role offers strong long-term career acceleration for researchers interested in healthcare AI, multimodal learning, and foundation model development. Experience gained here directly transfers to leading AI labs, biotech AI divisions, and regulatory-compliant ML systems development.

📋 Key Responsibilities

You will lead the development of self-supervised learning systems for medical imaging, designing scalable architectures using Vision Transformers and multimodal fusion techniques. Responsibilities include building training pipelines for MRI datasets, conducting rigorous experiment design, and validating model performance against clinical benchmarks. You will also define annotation standards and collaborate with radiologists to ensure high-quality labeled datasets. A key responsibility is deploying production-grade models into clinical workflows with measurable diagnostic improvements. Additionally, you will explore generative AI and agentic systems to improve radiology workflows and research automation. The role requires balancing research innovation with clinical reliability, ensuring all outputs meet strict performance and safety standards.

🎯 Application Strategy

🧠 Application Optimization (Adaptive)

This section is optimized for senior ML roles requiring deep technical alignment with foundation model research and clinical AI systems.

🧠 Fit & Positioning Analysis

Evaluate your match before applying.

📅 Application Signals

Hiring momentum appears strong with active recruitment and no fixed deadline. Competition is expected to be high due to the global relevance of foundation models and medical AI. Candidates with strong publication records or prior clinical AI deployments may receive faster interview progression. Response times typically range from 1–3 weeks, but highly aligned applicants may move faster. As no formal deadline is listed, applications may close without notice once the pipeline fills.

🔗 Apply for this Job

Apply on Company Site

✅ Job Source & Verification

This job listing is sourced from official Prenuvo hiring documentation and reflects a high-credibility employer posting in the medical AI sector. Information was last reviewed and validated on 2026-06-16. Salary and eligibility constraints are consistent with publicly stated company hiring policies. Role details align with verified senior applied science positions in healthcare AI organizations operating in Canada.