Top Machine Learning Jobs in Vancouver

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.

Own vision, strategy, and roadmap for cost-optimization and energy-management products for fleets. Translate telematics and operational data into customer-facing features, run discovery, prototype MVPs, define success metrics, and ship end-to-end with cross-functional teams.

Lead partner engagement and POC portfolio for Agentic Trust, managing onboarding, execution, metrics, and cross-functional coordination to scale partner integrations and transition successful pilots to commercialization.

Lead engineering strategy and execution for GitLab's growth and monetization platforms including self-service purchase, billing, usage metering, entitlements, quote-to-cash, integrations (Zuora, Salesforce), and API-first scalable architectures. Align cross-functional partners, ensure reliability, compliance (SOX), and apply AI/ML for predictive pricing and usage forecasting to support usage- and impact-based pricing.

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

Manage a team of ML engineers to develop machine learning solutions for underwriting applications. Set technical strategy, mentor engineers, and collaborate with teams across the organization.

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.

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 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.

Manage the vision, strategy, and roadmap for Contract Intelligence AI products. Collaborate with teams to define requirements and market strategies, ensuring delivery of generative AI solutions.

Lead end-to-end product design for Clio's Fintech/payments area: conduct customer research, frame opportunities, prototype and ship AI-enabled workflows, improve design systems, measure impact, and mentor other designers.

Lead development of AI-powered GTM tooling and infrastructure to drive pipeline, conversion, activation, retention, and revenue. Own metrics, run experimentation, partner with Sales/Marketing/Engineering/RevOps, and deliver scalable systems and revenue-compounding platforms.

Lead a team of ML engineers to develop and improve fraud detection models, defining strategies and fostering collaboration across functions to ensure effective model integration.

Lead technical vision, research, and engineering for a unified computer vision platform. Define long-term research agenda, drive MLOps and deployment across edge/cloud, set performance metrics, manage and scale distributed teams, and translate product needs into scalable, production-ready visual intelligence solutions.

The Director of Product for Data Flywheel will lead the roadmap for data initiatives, ensuring quality and compliance across various engineering teams and initiatives. Responsibilities include managing data metrics, supporting data pipelines, and collaborating across teams to enhance infrastructure.

Lead a product portfolio for Clio's core case management experience, utilizing legal expertise to drive innovation and user adoption while simplifying attorney workflows with AI tools.

Lead Growth Product strategy and execution to drive customer acquisition, onboarding, and GTM efficiency. Manage a team of product managers, define measurable goals, collaborate with Engineering, Design, Marketing, Sales and Data, and build scalable systems (account scoring, TAM data, outbound automation, ML models) to improve pipeline conversion, CAC payback, and activation outcomes.

The job focuses on expanding teams in Protein Science, Drug Development, Manufacturing, Quality, and supporting roles in various scientific and engineering fields.

Lead development and production of underwriting and credit decisioning models across Cash App Borrow and Afterpay. Own full modeling lifecycle: problem formulation, feature engineering, training, calibration, experimentation, deployment, monitoring, and iteration. Build decision frameworks, agentic engineering workflows, and collaborate with cross-functional partners to align model behavior with business and regulatory goals.

Lead strategy, vision, and roadmap for a cross-functional squad across Everand and Fable. Drive product development for recommendations, search, and ranking systems, balance experiments and large investments, partner with engineering, design, research, and data to ship features that help readers discover books.

Serve as strategic partner to enterprise clients to drive ROI from fraud detection and AML solutions. Lead onboarding, monitor detection performance, advise on ML and rule-engine usage, coordinate with Product and Engineering, run QBRs, educate on best practices, and identify expansion and optimization opportunities.

Lead and grow an ML training & serving platform team, define and execute roadmap for model training, deployment, GPU infrastructure, and low-latency serving, drive operational health, partner with ML and infrastructure teams, and recruit and develop platform engineers.

Design, build, and operate production ML decision systems to detect and prevent payment fraud, account takeover, identity abuse, merchant/marketplace risk, scams, and other adversarial activity. Own end-to-end production lifecycle: data contracts, low-latency inference, batch scoring, feature quality, deployment, monitoring, incident response, rollback, and feedback loops. Develop AI-assisted workflows and reusable decision capabilities while partnering with modelers, product, compliance, and operations.

Design, build, and operate production ML signal systems—ranking, retrieval, recommendations, propensity, and next-best-action—covering feature/candidate generation, serving, experimentation, monitoring, and feedback. Define signal contracts (freshness, provenance, confidence), evaluate long-term impact (trust, fairness, compliance), and partner across product, data, and risk teams to deliver reusable customer-intelligence capabilities.