Top Machine Learning Jobs in Vancouver

The Applied Scientist will transform raw data into actionable insights by developing machine learning models for personalization, ranking, and optimization to enhance conversion and revenue. This role emphasizes building production systems, frameworks for experimentation, and communication with team members regarding technical challenges.

Embed with clients to gather requirements and design AI-driven solutions for fraud and fincrime. Architect, train, evaluate, and deploy custom AI agents and workflows, optimize model performance, establish risk metrics and evaluation pipelines, and collaborate with engineering/product teams to scale into production.

Lead the product vision for innovative AI technologies, translating research into marketable products, and collaborating with cross-functional teams.

Seeking a Research Scientist to develop AI models for enhancing 3D reconstruction processes, requiring strong skills in computer vision and neural reconstructions.

The Junior Data Scientist will design and develop data models using Machine Learning and Analytics for trading and compliance in finance.

Lead the data and ML product vision: define roadmap, prototype models on noisy behavioral datasets, translate signals into user-facing features, partner with engineering to productionize ML, and generate insights to inform product roadmaps.

Lead end-to-end product design for real-time insights, reporting, and analytics in a legal SaaS product. Translate complex data and domain logic into scalable, accessible UI patterns, prototype AI-powered features, conduct customer research, and partner with product and engineering to drive strategy, launches, and iterative improvements.

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 Vegetation Intelligence product, owning roadmap from ML detection models to customer-facing risk products. Advance detection models, integrate outputs into outage and ignition risk, run user research, and translate model capability into actionable B2B products used in the field.

As a Research Intern, you will prototype and productionize research in AI and cryptographic schemes, collaborating with teams for real-world impact.

As an AI Engineer, you'll work closely with customers to understand their needs, train and deploy models, and build scalable AI systems.

Participants are needed for an AI technology research study, offering compensation up to $600 for ~1.5-2 hours of participation over one week, requiring skills in tech-related roles.

Lead end-to-end delivery of large-scale, technically complex customer programs for Phaidra's industrial AI control systems. Own program strategy, customer relationships, cross-functional coordination, risk mitigation, documentation, and continuous process improvements while mentoring TPMs and aligning product and engineering to customer needs.

Own end-to-end analysis of payments, chargebacks, and user lifecycle to identify drivers of net loss. Deep-dive into fraud cases, design and evaluate A/B experiments and risk-rule changes, build monitoring dashboards, partner with Risk, Payments, and Product to convert insights into mitigations, and run ad-hoc analyses of emerging loss trends.

Design and build production-grade ML systems for LLM-powered agents: implement RAG pipelines, orchestration, deployment, monitoring, and continuous improvement at scale while partnering with product, software, and data teams.

Lead delivery of ML feature platform initiatives: design, build, and operate backend systems for feature creation, storage, backfilling and serving. Collaborate with product, design, and analytics, own quarterly goals, improve platform reliability and code quality, mentor engineers, and support on-call and operational monitoring.

Design and implement scalable data pipelines and ML algorithms to optimize StackAdapt's digital advertising platform. Collaborate with teams to solve complex problems.

Develop and maintain simulation scenes, implement motion-planning and collision-checking in ROS2/MoveIt2, build perception components for detection and point-cloud processing, write tested C++/Python code, debug software-hardware integration, and participate in code reviews, CI, and agile practices in onsite R&D/production facilities.

Design and build scalable .NET backend services and gRPC microservices; optimize data layers (SQL Server, MongoDB, Elasticsearch); implement cloud integrations on AWS and Azure; ship AI/ML features (LLMs, RAG, vector search, ranking, personalization) and operate MCP servers; contribute to CI/CD, observability, containerization, and production ML integrations impacting high-traffic consumer marketplaces.

The role involves designing AI models and training methods leveraging wafer-scale hardware and collaborating with various teams and partners in AI and computational science.

The R&D Engineer will design and implement performance workloads on Cerebras' wafer-scale hardware, collaborating across teams to advance AI and scientific computing. Responsibilities include optimizing performance models, contributing to the technology roadmap, and publishing research findings.

Lead the pricing intelligence, supplier performance, and financial reconciliation for travel segments, utilizing AI and model-driven systems.

The role involves conducting research and optimizing machine learning systems while collaborating with technical staff to enhance product performance.

As a Research/ML Engineer, you will translate cutting-edge AI research into product solutions, develop safety and reliability systems, and collaborate with technical teams.