Top Machine Learning Jobs in Vancouver

Drives new and expansion revenue among small and medium-sized business fleet customers. Responsibilities include prospecting, qualifying leads, conducting discovery conversations, delivering product demonstrations, managing opportunities in Salesforce and Salesloft, using partner and referral programs, and collaborating with revenue operations, marketing, and account teams. The role requires strong communication, organization, sales aptitude, customer relationship skills, and interest in developing expertise in telematics and fleet management.

Build and maintain AI-enabled DevOps tools, pipelines, portals, analytics, automation workflows, knowledge frameworks, and lab environments for technical documentation and training teams. Develop AI agents and MCP servers, administer AI infrastructure and models, monitor token and GPU usage, improve publishing automation and GitLab workflows, optimize infrastructure reliability, and troubleshoot DevOps systems. The role collaborates with product, engineering, QA, documentation, and courseware teams.

Develop and maintain embedded software for FortiGate firewall SD-WAN features, including routing protocols, TCP/IP networking, policy-based routing, ZTNA, and AI-based routing. Responsibilities include designing efficient code, improving software performance and reliability, researching emerging technologies, troubleshooting complex issues, and enhancing development tools and processes.

Participate in RBC Amplify, a summer innovation program, as a Data Engineer on a cross-functional team. Develop and troubleshoot a functional prototype addressing a real business challenge, using data engineering, transformation, cleaning, mining, and modern data science techniques. Collaborate with mentors and stakeholders, engage in training, apply agile and design-thinking practices, and present the final product to executives and business leaders.

Build and support secure full-stack web applications, internal tools, APIs, data pipelines, and database schemas. Develop AI agents and LLM-powered solutions using Agentic AI, RAG, tool calling, guardrails, and Microsoft Foundry. Deploy machine learning models for predictive analytics, implement automated testing and CI/CD, resolve application and data issues, and modernize legacy systems. Collaborate with technical teams and business stakeholders to deliver scalable information and decision-support solutions.

Own NerdWallet’s machine learning product roadmap from opportunity discovery through production and adoption. Partner with Data Science, ML Engineering, Data Engineering, Product Engineering, and business teams to define objectives, prioritize initiatives, manage technical dependencies, evaluate model performance, and measure business impact. Lead cross-functional delivery, influence senior stakeholders, establish ML product strategy, and mentor product managers and partners without formal authority.

Builds and ships production code, customer integrations, data pipelines, and analytics tools for enterprise accounts. Serves as the technical lead for several clients, gathering requirements, prototyping solutions, owning system performance and service levels, and communicating with technical and executive stakeholders. The role requires hands-on software engineering, cloud and data-platform experience, schema design, production monitoring, and approximately 30% travel to customer sites across the United States.

Build and deploy production-grade AI backend systems integrating LLMs, traditional machine learning, vector databases, and RAG pipelines. Develop performant Python services, optimize inference and background processing, debug cross-layer issues, and ensure robust testing, monitoring, error reporting, reliability, and scalability. Collaborate with product, backend, and frontend teams to deliver practical AI applications.

Own enterprise sales across Western Canada by expanding existing accounts, acquiring new logos, managing the full sales cycle, developing territory strategies, building channel relationships, forecasting pipeline, and closing technology deals. The role requires communicating networking, security, AI, and machine learning business value to executive and technical stakeholders while meeting revenue and pipeline targets.

Designs and owns production machine learning systems for game development and live operations. Responsibilities include querying large-scale data warehouses, exploratory analysis, statistical testing, feature engineering, experimentation, model deployment in cloud environments, and collaboration with engineering and product teams. The role also involves mentoring junior data scientists, communicating insights to technical and non-technical stakeholders, and promoting data-driven decision-making.

Leads multi-track digital programs and portfolios, directing project managers, delivery plans, budgets, risks, resources, and client relationships. Establishes AI-enabled delivery guardrails, human review processes, and measurable value metrics. Coaches teams across Agile, Kanban, Waterfall, and agentic methods; contributes to proposals, pitches, and portfolio growth. Requires strong commercial judgment, enterprise digital delivery experience, senior stakeholder management, and hands-on fluency with AI tools and modern software delivery workflows.

Deploys and customizes Palantir Foundry and AIP solutions for enterprise clients across multiple industries. Responsibilities include conducting client workshops, building data pipelines and operational workflows with Python and TypeScript, solving complex technical problems, training users, documenting solutions, and supporting project delivery. The role requires strong data engineering skills, client communication, analytical problem-solving, and approximately 5% travel in Canada and the US.

Owns the strategy and roadmap for semantic insurance data products supporting AI, analytics, automation, and enterprise applications. Defines canonical concepts, relationships, metadata, data quality standards, APIs, knowledge graphs, and AI-ready consumption patterns. Partners with engineering, architecture, data, AI, UX, customer success, and insurance stakeholders to translate complex P&C insurance needs into product capabilities, prioritize use cases, establish requirements, drive adoption, and measure business impact.

Develop machine learning models and detection rules for web security products. Analyze large datasets to distinguish legitimate from fraudulent traffic, improve detection accuracy, support production data workflows, troubleshoot distributed systems, and provide insights that guide product improvements and strategy.

Lead a globally distributed endpoint security engineering team developing visibility, prevention, and machine learning capabilities across Windows, macOS, and Linux. Own the technical roadmap, delivery, cross-platform protection parity, and machine learning direction. Mentor senior engineers, manage hiring and career development, collaborate with product and engineering teams, maintain platform vendor relationships, and represent the team through customer engagement, conference presentations, and technical research.

Leads and scales Jobber’s credit risk function across payments and lending. Responsibilities include managing credit risk analysts, setting risk appetite and policy, overseeing underwriting, monitoring, collections, forecasting, provisioning, stress testing, and model governance. Partners with Data Science, Finance, Legal, Compliance, Product, and funding partners to build AI-enabled decisioning, automate operations, meet Canadian and U.S. requirements, manage vendors, and report portfolio performance to executives and risk committees.

Designs end-to-end architectures and high-level designs for web security products and features, spanning configuration, reporting, runtime systems, analytics, and attack detection. Collaborates with product management and cross-functional teams to define requirements, guides engineering implementation, presents designs, and contributes security product expertise and technical roadmap direction.

Leads complex actuarial and strategic analytics initiatives in Personal Insurance pricing. Uses actuarial techniques, predictive modeling, statistical methods, and large datasets to identify opportunities, assess risks, improve pricing, and inform business strategy. Partners cross-functionally with pricing, underwriting, claims, distribution, and data science teams; presents recommendations to senior leaders; mentors junior staff; and drives innovative analytical solutions from problem definition through implementation.

Conduct research on AI systems and infrastructure, developing algorithms and frameworks to improve performance, scalability, efficiency, reliability, and automation. Collaborate with engineering, data science, product, academic, and industry teams; publish and present research; and mentor researchers and engineers. The role focuses on foundation models, post-training, agent systems, ML systems, distributed systems, AI accelerators, and autonomous infrastructure management.

Own end-to-end design for AI-powered playbook automations in Clio Work. Lead vision, strategy, prototyping, user testing, and polished production experiences for complex legal workflows. Partner closely with Product and Engineering, shape trust and control in AI interactions, connect experiences across products, evolve the design system, communicate ideas to leadership, and mentor junior designers through critique and collaboration.

Verify inference accelerator ASIC designs at block and SoC levels using advanced methodologies, random stimulus, functional coverage, assertion-based verification, SystemVerilog, and UVM. Develop testbenches, verification infrastructure, reference models, and automated flows; debug designs; collaborate with architects, designers, and pre- and post-silicon teams; and improve verification productivity through innovative techniques.

Lead Huawei’s graphics standards team and define enhancements for future graphics APIs. Propose performance-focused API improvements, collaborate with standards organizations and internal product teams, and develop maintainable graphics solutions. The role requires deep computer graphics and API expertise, Khronos/W3C standards experience, technical leadership, knowledge of emerging rendering and machine learning technologies, parallel or distributed computing experience, and strong publications and communication skills.

Conduct empirical AI safety research with mentor guidance, owning projects from hypotheses and controlled experiments through analysis and technical writing. Build and debug evaluation harnesses, agent environments, datasets, monitoring methods, and post-training experiments. Investigate model failures, alignment, deception, misuse, multi-agent risks, and evaluation reliability. Produce reproducible code, experiment logs, research analyses, and papers for potential submission to leading machine learning conferences.

Leads pre-sales discovery, solution architecture, strategic advisory, scoping, SOW development, RFP responses, and deal strategy for large-scale data, AI, and cloud transformations. Designs GCP-based data, ML, GenAI, and agentic solutions; facilitates executive workshops; shapes industry-specific offerings; supports opportunities exceeding $1 million; and serves as a trusted technical advisor to C-suite and architecture stakeholders.

Develop AI/ML strategy and production software for a healthcare data platform. Design architectures, implement semantic matching, RAG, document parsing, agentic systems, knowledge graphs, and evaluation workflows. Write clean, tested, documented code; conduct reviews and pair programming; collaborate with clients and product teams; mentor developers; and contribute to technical strategy and Agile delivery.