Top Machine Learning Jobs in Vancouver

The Technical Product Manager will lead product direction, interface with engineering teams, and ensure the successful deployment of AI-powered safety systems. Responsibilities include defining product vision, translating customer needs, and maintaining high product quality.

The role involves leading AI/ML technical planning, evaluating new technologies, collaborating with stakeholders, and fostering innovation in the team.

Lead research, design, and implementation of large-scale cloud data ingestion, preprocessing, distributed training, and benchmarking for geospatial foundation models focused on agriculture. Contribute to model architecture, fine-tuning, tooling, documentation, and Agile development processes.

Provide embedded legal counsel to product and engineering on privacy, IP, and consumer protection; lead Arena's global privacy program (GDPR, CCPA/CPRA, cross-border transfers); advise on AI governance and responsible deployment; support commercial contracts and data flow frameworks; build privacy processes, playbooks, internal agents, and legal-tech integrations to scale operations.

As a Research Engineer, you will develop and enhance AI algorithms for self-driving vehicles, collaborating with scientists to advance technology and support deployment in production systems.

Lead design, development, and integration of AI/ML features into FortiAnalyzer for security analytics, anomaly detection, and automated response. Manage software developers and data scientists, oversee full ML pipeline (data, training, deployment, optimization), ensure production scalability and security, and collaborate with product, security, and engineering teams.

As a founding Data Scientist/Machine Learning Engineer, you'll develop AI/ML models, enhance product capability, and drive impactful user outcomes while working closely with product teams.

As a Senior Machine Learning Engineer, you'll design and develop AI systems, focusing on LLMs and improving agent performance while mentoring engineers.

Design, build, and operate scalable backend services in Python or Go to power underwriting decisioning. Integrate statistical and ML models, implement APIs (REST, gRPC/GraphQL), ensure observability and testing, lead projects, and collaborate with product, data science, and underwriting teams to automate underwriting workflows.

Build and deploy applied machine learning models and data-driven systems that classify, cluster, label, and enrich Internet-observed assets. Develop feature pipelines, training datasets, evaluation frameworks, confidence scoring, and production inference services. Collaborate with engineering, research, security, and product teams to transform raw Internet telemetry into high-quality datasets and customer-facing insights.

Industry Expert who reviews and defends advanced AI, ML, distributed systems, and forward-deployed engineering projects. Responsibilities include challenging architecture decisions, chairing mock review boards and executive presentations, hosting one or two technical sessions monthly, and serving as the escalation point for instructors. The role requires production-scale engineering judgment, strong technical communication, and expertise in agentic systems, LLM evaluation, infrastructure, observability, model serving, RAG, or enterprise deployments.

Develop and optimize auto-labelling systems using ML and computer vision, manage data pipelines, and collaborate with researchers.

The Forward Deployment Engineer will deploy and optimize Generative AI solutions, architect scalable infrastructure, and implement data pipelines across multi-cloud environments while collaborating with cross-functional teams to maximize business value.

The Senior Account Executive will develop relationships with utility clients, lead sales initiatives, negotiate contracts, and maintain sales activities in Hubspot to achieve sales targets.

Develop the next generation AI compiler for Mythic's analog compute, optimizing for dataflow architectures and collaborating with hardware engineers.

Develop and scale machine learning training systems for robotics data, optimize training pipelines, and collaborate with ML researchers to enhance autonomy models.

Bay Area-based hunter Enterprise Account Executive responsible for prospecting, developing, and closing new enterprise business for Anyscale. Manage complex sales cycles, achieve quotas, lead CxO engagements, qualify opportunities, collaborate with marketing, and drive ongoing account growth while advising customers on ML, compute, and cloud solutions.

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.

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

Develop scalable web applications using React and Next.js, implement backend solutions in Python, maintain DevOps practices, and collaborate with AI/ML teams to deploy applications.

The role involves designing and implementing machine learning systems for knowledge representation and temporal reasoning, focusing on organizational intelligence. Responsibilities include building LLM-powered pipelines, developing memory algorithms, and researching various ML techniques.

Lead enterprise AI strategy and roadmap; build and scale cross-functional AI/ML teams; architect and deliver LLM, RAG, predictive and data-heavy AI products; establish MLOps, governance, cloud-native platforms (Azure/Databricks/AWS/GCP); drive product lifecycle, KPI tracking, security, and responsible AI practices.

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

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.