Top Machine Learning Jobs in Vancouver

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.

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.

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 full-stack development for Toast IQ, owning user experience, backend services, and product integrations. Drive architecture, mentor engineers, participate in design and code reviews, and apply ML/LLM capabilities to improve product velocity and reliability.

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.

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

The Enterprise Account Executive will manage the complete sales cycle, build relationships with senior leaders, and drive platform adoption within data-intensive organizations.

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 AI-driven automation for risk and compliance by defining strategy, roadmaps, and metrics. Partner with ML engineers, data scientists, design, legal, and operations to build, evaluate, and deploy safe, reliable automations; run experiments, define success/failure behaviors, and drive cross-functional execution to improve decision quality and operational efficiency.

Lead strategy and execution for AI-powered risk automation products. Identify automation opportunities, define product requirements, partner with ML/data/engineering/design, run experiments, set metrics for accuracy, safety, and reliability, and drive cross-functional launches to improve decision quality and operational efficiency.

Lead teams in building end-to-end AI products focused on computer vision and sensor data. Oversee model lifecycle, data management, and deployment. Drive innovation in AI while fostering team development and ensuring customer success.

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 a team of Machine Learning Engineers and Data Scientists to design and deliver ML systems at Remitly. Collaborate across domains to implement MLOps best practices and contribute to AI strategy.

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.

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

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.

Lead engineering to build a unified platform for agent interactions with Workday Data Cloud, managing teams, and ensuring high-quality software delivery.

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.

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 end-to-end design of a new AI product: craft polished, human-centric interfaces, ship code directly, collaborate with researchers and ML specialists, and use AI tools to accelerate outcomes and deliver customer value each sprint.