Machine Learning Engineer

Join MetaOptima to make a difference in the lives of millions as we revolutionize the dermatology industry with software, hardware and AI for faster, more effective, and affordable care. MetaOptima is full of passionate, innovative people who thrive on working together as a team to build smart, life-saving technologies for medical professionals and their patients. Our casual, open-office culture welcomes fresh ideas from the minds of doers. Join us to add your voice to our vision while working with a cool group of people set on making their mark on the world. We take pride in what we’ve accomplished together, and can’t wait to see how you’ll help us grow.Location:Vancouver, BC - DowntownAbout MetaOptima:
Join MetaOptima to make a difference in the lives of millions as we revolutionize the dermatology industry with software, hardware and AI for faster, more effective, and affordable care. MetaOptima is full of passionate, innovative people who thrive on working together as a team to build smart, life-saving technologies for medical professionals and their patients. Our casual, open-office culture welcomes fresh ideas from the minds of doers. Join us to add your voice to our vision while working with a cool group of people set on making their mark on the world. We take pride in what we’ve accomplished together, and can’t wait to see how you’ll help us grow.Job Summary:
As a Machine Learning Engineer, you will join a small, high-impact R&D team and own the full ML lifecycle — from research and rapid prototyping through data pipelines, model training, and cloud deployment. Your work will directly shape clinical-grade computer vision systems that help dermatologists and their patients around the world.Responsibilities
Take vision problems from business needs through prototype to production system: data acquisition, training, evaluation, and deployment on AWS or GCP.
Rapidly prototype research ideas and estimate the effort required to develop an MVP.
Develop tooling to clean, label, and manage medical image datasets.
Analyze MVP performance to determine how to scale solutions for full cloud deployment on AWS or GCP.
Optimize models for inference and keep them running reliably once deployed.
Present findings clearly to both technical and non-technical stakeholders.
Qualifications (must‑have)
Master's or PhD in Computer Science or a related quantitative field, plus 2+ years of full-time industry experience building production-grade computer vision and machine learning systems.
Strong software engineering fundamentals: version control, testing, and code review, with the discipline to maintain several models and services at once rather than one project at a time.
Demonstrated end-to-end ownership of ML systems in production: data extraction and exploratory analysis, modeling and training, evaluation, drift detection, and serving.
Strong proficiency in Python, PyTorch, and ML/scientific computing libraries (scikit-learn, scipy, numpy), including optimizing models for inference with ONNX, TensorRT, or quantization.
Breadth across computer vision, not just deep learning (feature detection and matching, camera geometry and calibration, and registration) and the judgment to know when a classical method beats a learned one.
Owned ML services running in production on AWS or GCP, containerized with Docker, orchestrated on Kubernetes, and shipped through CI/CD.
Experience designing ML approaches, choosing the right metrics to evaluate them, and iterating toward a solution that moves a clinical or business outcome rather than a benchmark.
Self-starter with strong communication, documentation, and presentation skills.
Able to work effectively in a fast-paced startup environment.
Preferred Qualifications:
Experience with 3D vision and/or multiview geometry.
Experience in clinical or regulated environments
Why Join Us
You’ll play a pivotal role in advancing next-generation imaging technology for dermatology, helping transform how skin is captured, reconstructed, and analyzed worldwide.If you’re excited to turn cutting-edge machine learning research into clinical-grade imaging technology, we’d love to hear from you.
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