Top Machine Learning Jobs in Vancouver

Leads compliance technology oversight for transaction monitoring, sanctions screening, customer risk scoring, and AI-driven case resolution models. Builds AI-powered automation, governance frameworks, human-in-the-loop controls, real-time monitoring dashboards, data-quality controls, and model performance processes. Partners with engineering, machine learning, policy, regulators, and independent monitors to ensure models and data pipelines are explainable, auditable, exam-ready, and aligned with AML requirements across fiat and virtual currency products.

Lead technical strategy and development of Confluence AI experiences across frontend, backend, and AI integrations. Design scalable systems, apply generative AI, retrieval, agents, evaluation, and safeguards, and improve product quality, adoption, trust, and customer value. Drive complex initiatives from architecture through launch, partner cross-functionally, mentor engineers, establish quality practices, and guide experimentation and continuous improvement across Atlassian.

Develop and scale production machine learning models for credit decisioning, including default prediction and approval decisions. Build training, decisioning, monitoring systems, data pipelines, features, and algorithms. Conduct research, develop prototypes, and collaborate with engineering, credit, and product teams to deliver machine learning products from inception through business impact.

Conduct advanced research on large foundation models and post-training techniques for specialized domains. Develop AI-driven systems, research prototypes, and software tools; collaborate with researchers, engineers, and cross-functional teams; publish findings in leading conferences and journals; and present work at industry events.

Conduct research on AI systems and infrastructure, developing algorithms and frameworks to improve performance, scalability, efficiency, reliability, and automation. Collaborate with engineering, data science, product, academic, and industry teams; publish and present research; and mentor researchers and engineers. The role focuses on foundation models, post-training, agent systems, ML systems, distributed systems, AI accelerators, and autonomous infrastructure management.

Lead Huawei’s graphics standards team and define enhancements for future graphics APIs. Propose performance-focused API improvements, collaborate with standards organizations and internal product teams, and develop maintainable graphics solutions. The role requires deep computer graphics and API expertise, Khronos/W3C standards experience, technical leadership, knowledge of emerging rendering and machine learning technologies, parallel or distributed computing experience, and strong publications and communication skills.

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.

Designs and owns production machine learning systems for game development and live operations. Responsibilities include querying large-scale data warehouses, exploratory analysis, statistical testing, feature engineering, experimentation, model deployment in cloud environments, and collaboration with engineering and product teams. The role also involves mentoring junior data scientists, communicating insights to technical and non-technical stakeholders, and promoting data-driven decision-making.

Build infrastructure and tooling for AI evaluations, including distributed scoring engines, task-set validation, grader systems, human-review calibration, statistical leaderboards, online feedback capture, and offline-to-online evaluation loops. Partner with product, engineering, data, and ML teams to deliver reliable, observable evaluation workflows that support faster and more trustworthy AI product development.

Designs, develops, and deploys scalable production AI features and shared services. Builds backend APIs integrating LLMs, RAG, AI agents, machine learning, deep learning, and computer vision. Applies AI-native engineering and agentic SDLC practices, including coding agents, specification-driven development, automation, testing, and monitoring. Collaborates across product and engineering teams, contributes to architecture and technical decisions, mentors engineers, and improves AI reliability, performance, security, and cost efficiency.

Lead a team of 4–8 engineers developing production-ready AI features and shared platforms. Responsibilities include architecture, coding, generative AI, machine learning, computer vision, LLMs, RAG, AI agents, workflow orchestration, and AI-native software development. The role drives Agentic SDLC adoption, AI-assisted engineering workflows, technical standards, responsible AI deployment, and collaboration with Product, Security, Compliance, and engineering teams.

Supports biomedical research by preparing, cleaning, integrating, analyzing, visualizing, and reporting clinical, molecular, genomic, and imaging data. Develops basic scripts and analytical tools, performs quality checks, maintains research data and computational resources, documents workflows, and contributes to reports, presentations, publications, and grant applications under senior team guidance.

Build predictive models and machine learning applications that generate actionable business insights. Analyze large datasets, develop statistical models, optimize data architectures and warehouses, standardize data models, and identify trends or anomalies. The role partners with business strategy to use AI and data to improve efficiency, sales, profitability, customer experience, and operations.

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.

Leads technical direction for GitLab’s Core DevOps services, covering CI, CD, planning, and source control. Defines architecture, quality metrics, migration strategies, and technical roadmaps; addresses systemic risk and complex design decisions; prototypes and writes code when needed; partners with Infrastructure, Security, SRE, and Product; adopts AI capabilities; mentors senior engineers; supports incident response and senior technical hiring.

Design and implement scalable data pipelines and ML algorithms to optimize StackAdapt's digital advertising platform. Collaborate with teams to solve complex problems.

Develop data science solutions for payments across Square and Cash App. Build AI-powered tools, ETL pipelines, dashboards, machine learning models, experiments, and cloud data systems. Partner with Product, Engineering, Finance, and Operations to improve payment success, cost efficiency, risk mitigation, and decision-making. Translate business needs into practical solutions, research questions independently, and operationalize data processes and infrastructure.

Develop data tools, AI automations, ETL pipelines, dashboards, and machine learning models for payments. Analyze experiments, optimize payment performance and costs, mitigate risk, maintain data systems and cloud infrastructure, and translate business needs into actionable solutions while partnering with Product, Engineering, Finance, and Operations.

Leads vision, strategy, and roadmap execution for Workday’s AI-powered Contract Intelligence and Analytics portfolio. Partners with engineering, data science, design, marketing, customers, and executives to develop generative and predictive AI products, define requirements, analyze markets, establish monetization strategies, and deliver enterprise SaaS solutions at scale.

Build and operate scalable full-stack software and AI-powered products for IoT-driven physical operations. Responsibilities include defining technical strategy, developing backend services, APIs, frontend and potentially mobile features, transforming AI capabilities into customer products, partnering across functions, leading complex projects, mentoring engineers, and improving engineering standards.

Leads enterprise data strategy execution, architecture, platform implementation, governance, and significant data initiatives. Designs scalable data solutions, models, integrations, cloud platforms, and AI-enabled capabilities. Partners with investment, operations, and technology teams to translate business needs into sustainable solutions. Leads and develops data professionals, improves reliability and reuse, and builds operational leadership resilience across enterprise data responsibilities.

Own the technical side of deal progression for airline deployments: qualify opportunities, run demos and POCs, author integration and deployment architectures (PSS, APIs, data feeds, DNS, identity, networking), respond to RFPs/security questionnaires, scope SOWs and NFRs, and build reusable demo/reference assets to drive adoption and expansion while liaising between sales, engineering, and customers.

Own product-to-field marketing and enablement for Unified Access, SaaS Manager, and multi-product expansion. Develop cross-product narratives, sales materials, training, discovery guidance, and cross-sell strategies for enterprise buyers. Partner with Product, Engineering, Sales, Customer Success, Technical Marketing, and Demo Engineering; support strategic customer opportunities; coach sellers; and use field feedback, win-loss analysis, and customer insights to improve messaging, launches, and product decisions.

Leads Duck Creek Technologies’ Intelligence function, overseeing analytics engineering, business intelligence, reporting, dashboards, metrics, and insight generation. Builds scalable analytics platforms and governance standards, partners with product, AI, engineering, and business leaders, and translates complex data into actionable recommendations. Manages analytics teams and advances enterprise data-driven decision-making, operational performance, and product development.

Leads company-wide data science priorities, KPI frameworks, measurement standards, causal modeling, scenario planning, and agentic analytical automation. Builds reproducible SQL and Python workflows, validates predictive and causal relationships, establishes governed self-service analytics, and partners with Finance, Product, Commercial, Engineering, and Data Engineering. The role drives trusted metric adoption, improves decision speed, mentors scientists, and aligns leadership on targets, trade-offs, uncertainty, and investment decisions.