Top Machine Learning Jobs in Vancouver

Supports North American sales by serving as the technical expert for Mastercard Identity solutions. Responsibilities include customer discovery, solution design, technical presentations, proof-of-value testing, training, documentation, conference participation, and customer feedback for product development. The role requires explaining machine learning, data science, APIs, and web architecture to technical and nontechnical stakeholders while advising customers on fraud detection and identity solutions.

Lead and deliver complex, cross-functional fraud programs end-to-end: translate fraud signals into technical/product requirements, run risk assessments, own incident investigations and mitigation, partner with engineering and analytics, define metrics, and apply AI to automate and scale fraud controls.

Design, implement, test, deploy, and operate highly available distributed services for Azure Monitor at global scale. Build telemetry collection, routing, storage, and data-access APIs and tools; improve reliability, security, observability, and diagnostics; and integrate machine learning and AI-assisted operational capabilities. Participate in on-call and incident response, collaborate across teams, mentor engineers, lead design reviews, and drive architectural alignment.

As a Staff Machine Learning Engineer, you will enhance credit modeling, manage full credit modeling stacks, and support innovations within regulated banking frameworks.

Lead independent validation and ongoing monitoring of complex financial and analytics models, including ALM, loss forecasting, loan transition, corporate planning, and engagement models. Identify weaknesses, communicate findings, guide remediation, and strengthen model risk governance. Partner with Finance, Quantitative Research, Growth Analytics, Internal Audit, Internal Controls, Accounting, and Compliance on validation, audit, regulatory, and examiner matters.

Lead product strategy for consumer credit, underwriting, merchant pricing, and loan economics. Define features, specs, and metrics; partner with engineering, analytics, design, commercial, legal, and capital to launch and scale products across channels and geographies.

As a Staff ML Engineer, you'll design and operate Samsara's ML platform, partnering with teams to deliver scalable ML solutions that enhance safety and efficiency in physical operations.

Lead and grow a data science team to build merchant registry and profiling capabilities. Define strategy, drive ML model development and deployment at scale, establish best practices, partner with Product and Engineering, and deliver measurable business outcomes while promoting reusable assets and Agile delivery.

Leads the design, automation, deployment, and operation of a secure medical imaging pipeline for CT data across Canadian research sites. Responsibilities include Python and DICOM development, PACS integration, de-identification, XNAT administration, Linux server and GPU environment management, privacy compliance, ethics submissions, stakeholder coordination, technical reporting, procurement, and milestone delivery. The role independently manages complex multi-institutional implementation and may supervise junior staff.

Own the vision, strategy, and execution of Match Group’s Signals Platform, standardizing behavioral, identity, trust, safety, and enforcement data across global brands. Partner with engineering, data science, compliance, operations, and product teams to build APIs, data products, governance frameworks, and adoption models. Define metrics, prioritize investments, improve fraud and abuse detection, evaluate AI and ML opportunities, and drive scalable platform adoption and measurable business outcomes.

Own customer and vendor contracting, including MSAs, DPAs, NDAs, security terms, and data licensing agreements. Build templates, negotiation playbooks, fallback positions, and approval processes. Advise on privacy, security, AI governance, corporate matters, employment, equity, partnerships, and insurance regulation. Manage external counsel, support SOC 2-related contractual work, and partner with Sales, Operations, Product, Engineering, and executives on commercial and legal risks.

Lead the underwriting and credit risk validation sector within Model Risk Management. Manage and mentor a quantitative team, perform hands-on validations of complex ML models, assess data integrity and model performance, identify and remediate weaknesses, and partner with stakeholders to enhance the enterprise MRM framework and meet audit and regulatory requirements.

Lead cross-organizational technical strategy and execution for core sharing and collaboration systems. Architect and implement foundational frontend/backend and platform solutions at massive scale, drive AI/LLM adoption, mentor engineering leaders, resolve systemic bottlenecks, and remain hands-on in critical design, prototyping, and code contributions.

Own strategic enterprise robotics accounts from technical discovery through pilots, deployment expansion, and commercial growth. Translate customer operations into technical scopes, ROI models, deployment requirements, and acceptance criteria. Explain robot learning capabilities to operations and ML leaders, qualify technology partnerships, manage forecasts and expansion pipelines, and feed customer requirements into product and research roadmaps. The role requires cross-functional project ownership, consultative enterprise engagement, and fluency in robotics, industrial automation, and modern machine learning.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

Develop and deploy statistical and machine learning models for credit risk, fraud, collections, and marketing. Responsibilities include data exploration, model development, production monitoring, performance analysis, governance, stakeholder collaboration, and communicating insights to leadership. The role requires applying advanced statistics and machine learning to improve consumer finance strategies while maintaining documentation, quality assurance, and version-control standards.

Deploy onsite with strategic customers to understand security and risk workflows, build AI-native solutions, analyze data, and integrate SecurityScorecard with enterprise systems. Design cloud architectures across AWS, Azure, and GCP; prototype and productionize software; translate security data into financial risk insights; and maintain technical relationships through renewals and expansion. Travel to customer sites for workshops, technical discovery, and go-live support.

Annotate and manage training/test datasets, run model training, evaluate model performance, perform manual QA and error analysis, and refine annotation guidelines for NLP/ML projects.

Supports responsible AI governance by developing policies, maintaining control frameworks, conducting AI risk assessments, monitoring regulatory compliance, preparing reports, and advising legal, compliance, security, AI, and business stakeholders. The role also tracks governance metrics, supports audits, researches emerging regulations, and recommends improvements to AI governance programs.

Monitors and improves the performance, reliability, and adoption of AI-powered systems. Analyzes operational metrics, develops dashboards and reports, identifies bottlenecks, supports incident resolution, documents procedures, and recommends process improvements. The role collaborates with AI engineers, product teams, business stakeholders, and operations leaders while supporting governance, compliance, risk mitigation, and continuous improvement initiatives.

Leads the strategy, roadmap, development, launch, and optimization of AI-powered products. Defines product requirements, priorities, success metrics, and business outcomes while coordinating engineering, AI, executives, and business stakeholders. Evaluates emerging AI technologies, supports responsible AI governance, manages feedback and adoption, and drives continuous product improvement.

Leads AI data annotation operations, managing workflows, resources, productivity, quality assurance, and project timelines. Coaches and develops annotation teams, supports recruitment, and collaborates with AI engineers, trainers, and product teams. Establishes annotation guidelines, conducts audits, resolves quality issues, and drives process improvements, automation, tool adoption, and operational excellence across AI data initiatives.

Develop and manage the strategy, roadmap, lifecycle, and delivery of AI-powered products. Translate business needs into product requirements, collaborate with engineering and AI teams, define success metrics, monitor adoption and ROI, and drive continuous improvements. The role also engages customers and stakeholders, evaluates emerging technologies, supports governance and compliance, and promotes responsible AI practices.

Own AI product backlogs, priorities, user stories, acceptance criteria, and delivery outcomes. Translate business needs into product requirements, collaborate with technical teams on solution design, facilitate stakeholder communication, support Agile delivery, and monitor product performance and adoption. The role also identifies AI-driven improvements, supports product releases, drives continuous improvement, and promotes responsible AI practices.

Leads business analysis for AI and automation initiatives by identifying adoption opportunities, developing business cases, gathering requirements, facilitating stakeholder workshops, supporting solution design and delivery, measuring benefits, and contributing to AI governance and continuous improvement.