Top Machine Learning Jobs in Vancouver

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 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 scientific and strategic AI efforts for Vaccines Medical: define AI vision and roadmap, govern responsible AI standards, prioritize and deliver validated AI-enabled solutions, partner cross-functionally, oversee deployment and adoption, manage budget and vendors, and ensure regulatory, ethical, and scientific compliance across global medical affairs.

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.

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 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 end-to-end ML lifecycle: data exploration, model training, evaluation, deployment, and monitoring. Design and scale batch and real-time feature pipelines, partner cross-functionally to productionize models, implement MLOps (CI/CD, retraining, drift detection, observability), and mentor engineers to maintain high code and system quality.

Design, build, and deploy production-grade backend generative AI solutions. Convert ambiguous R&D into scalable, secure production software, own deployed code, optimize ML architecture and infrastructure, and collaborate with product and R&D teams to elevate technical capabilities.

Drive live-ops and monetization strategy for F2P mobile games: advocate for players, create events and offers, forecast content cadence, analyze data with A/B tests and ML, and coordinate cross-functional teams to optimize engagement and revenue.

Drive product roadmap and KPIs for free-to-play games. Define goals/OKRs, design and optimize in-app features and monetization, run A/B and multivariate experiments (with ML), analyze game economy using BI tools, and iterate on player engagement. Mentor team members and participate in recruitment.

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.

Drive product strategy and execution for ML-driven geospatial systems: translate research into production roadmaps, own execution frameworks, manage dependencies across data engineering, ML, and cloud teams, lead sprint rituals, and communicate technical tradeoffs and delivery status to senior leadership and external stakeholders.

Lead backend-focused product development for the Partnership Intelligence platform, owning data ingestion, enrichment, modelling, governance and tooling. Define roadmaps, write requirements, work with engineers, vendors and stakeholders, ensure data quality/observability and compliance, and enable downstream segmentation, reporting and valuation to drive commercial outcomes for sports and entertainment clients.

The Product Manager, AI Enablement will drive AI initiatives by analyzing operations, developing business cases, and ensuring responsible AI deployment across the organization.

Conduct R&D and implement state-of-the-art NLP and ML algorithms for real-time, agentic voice and chat systems. Build and deploy LLM-based, multilingual and multimodal features, manage large text datasets, evaluate model performance, collaborate with engineering and product teams, and publish research.

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.

Design, deploy, and maintain end-to-end ML risk solutions at scale to detect and prevent fraud, merchant risk, and credit loss. Partner with cross-functional teams, lead technical decisions, build ML tooling, monitor models in production, and investigate emerging abuse patterns to improve detection and decisioning.

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.

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.

As a Machine Learning Engineer II at Affirm, you'll develop AI systems for automating customer operations and collaborate on building models while ensuring high-quality code production.

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.