Top Machine Learning Jobs in Vancouver

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.

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.

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

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.

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 end-to-end personalization data science initiatives: design and deploy machine learning and statistical models into production, optimize recommendations and content layouts, partner with engineering/product/analytics, monitor performance, and mentor junior scientists.

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 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 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.

Lead strategy and execution for an AgTech SaaS platform, owning roadmap, grower workflows, data integration, IoT in-field solutions, and partner hardware/commercialization. Drive analytics, APIs, and platform capabilities with engineering, data science, and GTM teams to deliver actionable agronomic decision tools and revenue growth.

The specialist will enhance TRIUMF's research through Machine Learning and Quantum Computing, mentor students, lead software development for high-performance computing, and foster partnerships in academia and industry.

Own and set technical direction for the Everand backend, building scalable, reliable systems for discoverability, personalization, and core consumption journeys. Drive architecture, platform health (CI/CD, staging, DBs, queues, cost, observability), mentor engineers, lead cross-team planning, modernize legacy systems, and partner with product, design, analytics, web/mobile, and ML to support experimentation and telemetry-driven decisions.

Lead architecture and hands-on development of high-performance cloud and on‑prem streaming platforms. Drive technical vision, mentor teams, ensure scalability, low-latency media delivery, observability, CI/CD, and integration of machine-learning pipelines into production systems.

As a Senior/Staff Machine Learning Engineer, you will design scalable data pipelines, implement ML algorithms, and collaborate with engineers and Data Scientists to optimize the digital advertising platform.

Lead R&D on quantum-classical hybrid algorithms and system benchmarking. Design and run experiments, reproduce literature results, analyze QPU contributions, communicate findings, and mentor teammates to improve performance and scalability.

Lead enterprise data, analytics, AI, automation, and information governance. Define strategy and roadmap, modernize data platforms (lakehouse/Azure Databricks), establish AI and automation capabilities, enforce governance/MDM (Semarchy), deliver BI/Power BI and predictive solutions, run Automation CoE, drive data literacy, and build a high-performing cross-functional team.

The Senior Software Development Engineer will build backend services in Go on AWS, enhance AI/ML platform offerings, and drive infrastructure optimization for scalable products.

Support P&C pricing, underwriting and product development through data extraction, cleaning, actuarial analyses, rating model development, trend and loss ratio studies, and documentation. Collaborate with senior actuaries on research, predictive modeling, and decision support while progressing through CAS exams.

Apply machine learning and statistical methods to integrate geophysical, geological, geochemical, remote sensing and drillhole data for mineral prospectivity and geological classification. Deliver consulting projects and client reports, support platform development (SmartSuite), evaluate new ML methodologies, assist R&D, and collaborate cross-functionally while contributing to proposals, QA, and technical documentation.

Build and iterate underwriting ML models for real-time transaction decisions. Develop feature pipelines and training datasets, prototype and evaluate models, productionize into batch or real-time systems, and instrument model/data health, retraining, and monitoring. Collaborate with engineering, risk, product, and ML platform teams to deploy robust, low-latency solutions with appropriate risk controls.

Senior individual contributor building and maintaining underwriting and credit decisioning ML systems for Cash App Borrow and Afterpay. Responsibilities include feature engineering, model training, calibration, experimentation, deployment, monitoring, and portfolio-level analysis. Collaborate with cross-functional teams to align models with business and regulatory goals and develop AI-native engineering workflows and governance for reliable, auditable model development.

Develop and propose enhancements to graphics API specifications (Vulkan, OpenGL, WebGL/WebGPU, SPIR-V, glTF), apply computer graphics and GPU computing techniques, engage in KHRONOS/W3C standard activities, and contribute ML and parallel computing expertise with a strong publication record.

Build and maintain scalable, reliable ML data platform systems to support dataset generation, model training, analytics, monitoring, and large-scale inference. Collaborate with ML, software, and infrastructure engineers to design cost-efficient data lake and training infrastructure using vendor and open-source tools to enable next-generation ML models.

Design, deploy, and maintain end-to-end ML-driven risk solutions at scale to detect and prevent fraud, abuse, and credit risk. Lead technical decisions, build ML tooling and processes, apply state-of-the-art models and third-party data, investigate emerging risk patterns, and collaborate with platform and cross-functional teams to ensure reliable real-time model operation.