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 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 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.

The Global Head of Sanctions will lead Block's sanctions compliance program, manage escalations, regulatory reporting, and engaging with external agencies while developing and implementing sanctions policies.

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

The Staff Software Engineer will develop full-stack applications, lead projects, mentor teammates, and drive architectural discussions to enhance Toast's restaurant platform.

As Principal Product Manager, you'll define and execute the strategic vision for AI quality, enhancing enterprise search and AI products through cross-functional collaboration and customer insights.

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.

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.

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 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 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.

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.

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.

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.

As a Staff Machine Learning Engineer, you'll lead AI initiatives using large-scale data, optimize ML models for edge devices, and collaborate with cross-functional teams.

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.

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.

Lead engineering to build a unified platform enabling agentic access to Workday Data Cloud via a semantic layer and MCP server. Architect, implement, validate, and scale ML and agent systems, deliver mission-critical features on schedule, design APIs, and mentor engineering teams.

Lead the development of fraud prediction models, build feature pipelines, prototype modeling ideas, and productionize models while collaborating with cross-functional teams.

The Applied Scientist will develop next-gen AI systems for autonomous driving, focusing on machine learning, simulation, and robotics, advancing embodied AI technologies.

Lead sensitivity studies and simulation development for the XLZD liquid xenon observatory, applying Monte Carlo and machine learning techniques for 0νββ searches. Support calibration and detector R&D at TRIUMF, produce technical reports and publications, present results, and mentor students within an international collaboration.

Design, implement, and scale event-driven, distributed backend systems in Python on AWS to process and enrich large-scale document and media datasets. Lead technical projects, mentor engineers, integrate ML/LLM components, optimize performance, maintain observability, and collaborate with Data Science, ML Engineering, and Product to deliver reliable high-throughput content processing services.

Lead zero-to-one product initiatives to transform knowledge discovery using ML/LLMs. Define vision, roadmap, and success metrics; prioritize trade-offs; partner with Design, Engineering, ML, and Data Science; deliver incremental wins while building an extensible platform and improving prospect and subscriber experiences across devices.