Top Machine Learning Jobs in Vancouver

As an Applied AI Engineer at Luminai, you will design experiments, optimize machine learning models, and enhance AI platforms for healthcare automation.

The Hyperscalers and Commercial Lead will develop technology-driven business strategies, drive customer adoption, and manage end-to-end sales processes while working closely with cross-functional teams.

Drive independent, high-impact research on frontier AI topics, publish findings, collaborate widely, and mentor new researchers in the field.

Drive data-informed capital allocation by building financial models (NPV, IRR), applying AI/ML for forecasting and scenario planning, creating build-vs-buy and prioritization frameworks, implementing decision-support systems, collaborating with cross-functional stakeholders, and monitoring post-investment performance to refine allocation models.

Contract role combining actuarial science, risk management, and AI to improve portfolio performance. Responsibilities include loss and combined ratio analysis, portfolio risk assessment, catastrophe and exposure modeling, stress testing, and integrating ML to enhance pricing, reserving, and risk selection while collaborating with underwriting, finance, and product teams.

Design, implement, and monitor AI/ML models to improve underwriting risk assessment, pricing, and portfolio management. Translate underwriting rules into data models, integrate models into workflows, analyze datasets for risk patterns, build decision-support tools, ensure compliance and fairness, and collaborate with underwriting, actuarial, product, and engineering teams.

Design, implement, and optimize AI-powered solutions to support financial compliance and controls. Monitor GAAP/IFRS adherence, automate controls and reporting, support internal/external audits, and manage complex accounting areas like revenue recognition and lease accounting while collaborating with finance, audit, and technology teams.

Lead development of predictive pricing models using machine learning and actuarial methods; define data needs, ensure data integrity, collaborate with underwriting/claims/finance, own technical pricing, and mentor actuaries and data scientists.

The Product Manager, AI Enablement will drive AI initiatives by analyzing operations, developing business cases, and ensuring responsible AI deployment across the organization.

Lead product development for Maestro, an AI platform for mortgage origination and servicing. Translate operational workflows into PRDs, define metrics (throughput, time-to-decision, accuracy), collaborate with ML, engineering, underwriting ops, and stakeholders, build feedback loops between AI output and human judgment, and drive roadmap execution in a regulated, high-ambiguity environment.

Lead pre-sales efforts for Media & Entertainment customers: present Snowflake to executives and technical audiences, deliver demos and POCs, design solutions, collaborate with product and engineering, and close large deals while maintaining deep industry and competitive knowledge.

Lead finance analytics, own data warehouse and BI strategy, build Finance AI automation and model foundation, optimize GCP costs, create self-service analytics, ensure data governance and audit readiness, and manage a cross-functional team supporting FP&A and accounting.

The Senior Geo Data Scientist will develop advanced machine learning models for geospatial data, build scalable data pipelines, and enhance model performance for mineral exploration.

The Forward Deployed Research Scientist engages directly with AI labs to develop data strategies, fine-tune models, run experiments, and collaborate on publishing research, all in a fast-paced client-focused setting.

Lead cross-functional teams of machine learning engineers and data scientists, driving technical direction and fostering a collaborative environment to develop high-quality ML-driven capabilities.

Lead the pricing intelligence, supplier performance, and financial reconciliation for travel segments, utilizing AI and model-driven systems.

The role involves architecting AI solutions, optimizing ML systems, and deploying advanced retrieval frameworks. Engineers will lead ML lifecycle from research to production, collaborating across teams to build AI-first products.

The role involves analyzing biological mass spectrometry data, developing software tools, and scaling workflows for AI and human consumption in drug safety assessment.

This role involves leading the development of AI systems to replace lab toxicity experiments, focusing on end-to-end ML architecture, research on chemistry-biology correlations, and building innovative models. Candidates should have strong ML skills, engineering abilities in Pytorch and Python, and a passion for both science and technology.

The Senior Product Manager will set product direction, own the development lifecycle, collaborate with teams, and ensure high-quality outputs. This role requires strong opinions on product quality and customer insights.

The role involves developing and optimizing machine learning algorithms for computer vision, collaborating with teams on project requirements, and overseeing the development lifecycle for AI projects.

Conduct AI/ML literature and technology trend research, produce technology reports, evaluate and recommend cutting-edge AI/ML methods, collaborate with cross-functional teams to plan and implement AI projects, and promote innovation and continuous improvement within the AI/ML team.

As a Technical Recruiter at Lazer, you will source and screen technical talent, manage candidate communications, maintain records in the ATS, and automate sourcing tasks using AI tools.

The role involves building, deploying, and maintaining ML infrastructure, optimizing systems, and ensuring production reliability while collaborating with data scientists and product teams.

The Applied Scientist will drive research on efficient adaptive machine learning, deploy ML systems end-to-end, and contribute to product strategy, focusing on real-world impact and innovation.