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.

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 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.

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

Lead end-to-end growth marketing for 1Password's SaaS Manager: build demand gen and cross-sell programs, run experiments, partner with Product/PMM/Sales, leverage AI agents and automation to scale pipeline and create repeatable growth playbooks.

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 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 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.

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 research on AI-driven systems and networking to develop novel methods and algorithms that improve performance, scalability, and efficiency. Collaborate with engineers and product teams to deploy solutions, publish and present findings, mentor researchers, and partner with academia and industry on joint research.

Lead research on AI-driven systems and networking: develop algorithms and methodologies to improve performance, efficiency, and scalability; collaborate with engineering and product teams; publish and present findings; mentor researchers; and partner with industry and academia to drive system innovation and deployment.

Design, implement, and run highly reliable, scalable real-time media services and client/server streaming components for Teams/Skype. Improve performance using telemetry, testing, benchmarking, and apply big data and machine learning techniques to enhance media and communication systems.

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

Lead architecture and technical strategy for AI-driven product quality systems using LLMs and agents. Build scalable evaluation frameworks, detect regressions, generate insights, and drive cross-functional adoption while mentoring engineers and defining standards for trustworthy AI.

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.

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 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.

Independently validate and monitor machine learning models used for credit underwriting, credit risk, and fraud detection. Identify weaknesses, recommend improvements, and collaborate with model owners to remediate findings. Partner cross-functionally to implement and maintain the Model Risk Management framework and support audit, compliance, and regulatory requests.