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ML Team Lead & Senior ML Engineer · Vancouver, BC · Canada
Machine learning that holds up in production.
ML Team Lead and Senior ML Engineer designing ML-based systems for segmentation, classification, and regression across 2D and 3D medical imaging. My research focuses on robust visual learning under distribution shift.
ML Team LeadSenior ML EngineerML ResearcherFounder, careersimplify.com
Work experience
ML Team Lead · Synthesis Health
Scaling LLM and medical-imaging AI for production.
At Synthesis Health, we build production AI for clinical workflows: LLM systems alongside 2D radiography and 3D volumetric-imaging models. These services process thousands of requests every day in regulated, high-availability environments.
I lead the cross-functional team responsible for model development, validation, cloud inference, monitoring, and production operations. By optimizing model execution and serving architecture, I reduced production ML inference time by 7× while preserving the reliability required for clinical software.
View my experienceFounder · CareerSimplify
Founder, product builder, and operator
One AI-assisted workspace for the whole job search.
I founded CareerSimplify to replace the disconnected mix of job boards, spreadsheets, and duplicate CV files with one continuous workflow—from finding a role to sending the right application.
Its AI tools help job seekers create role-specific CV versions, improve summaries and experience bullets, identify relevant skills, analyze job fit, and generate matching cover letters without overwriting the base CV.
Visit CareerSimplifyFind & track
Discover roles, manage the application pipeline, and keep each job's CV, cover letter, notes, and timeline together.
Tailor with AI
Branch a role-specific CV, strengthen its content, surface matching skills, and generate a focused cover letter.
Export & apply
Export polished documents and retain the exact version attached to each tracked application.
Research
Recent research interests
Making visual models reliable when conditions change.
My current work studies how vision systems generalize beyond their training distribution, with an emphasis on methods that adapt efficiently and expose the evidence behind their predictions.
Domain generalization
Learning systems that remain reliable when deployment data differs from training.
Training-free few-shot learning
Adapting frozen visual models to new domains using local evidence and very few examples.
Adaptive visual models
Input-aware routing, mixtures of experts, and robust representations for changing conditions.
Selected publications
Publication record-
Variational Patch Gating for Training-Free Few-Shot Classification
Venue European Conference on Computer Vision (ECCV)
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Class-Conditional Cauchy-Schwarz Quadratic Mutual Information for Domain Generalization
Venue European Conference on Computer Vision (ECCV) Workshops
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MoE²: A Mixture-of-Mixtures of Experts for Ensemble-Free Domain Generalization
Venue Proceedings of the AAAI Conference on Artificial Intelligence
Open to new roles