Top Machine Learning Jobs in Vancouver

Own end-to-end technical delivery for a product capability, designing and building full-stack cloud-native applications, APIs, integrations, microservices, and AI-powered features. Lead a small development team through architecture, implementation, code review, delivery automation, mentoring, and production support. Apply algorithms, modeling, and data-driven methods while partnering with Product, Design, Engineering, and Operations in a complex enterprise environment.

Owns analytics and optimization strategy for advertising revenue across Zynga’s mobile game portfolio. Responsibilities include monitoring performance, diagnosing trends, designing A/B tests, identifying monetization opportunities, partnering with product, analytics, and data science teams, supporting AI and ML models, leading cross-functional initiatives, and mentoring analysts.

Drive pipeline and revenue growth through digital prospecting, lead qualification, relationship building, and SaaS sales. Engage prospective enterprise customers, manage inbound and outbound leads, use tools such as LinkedIn, 6Sense, Outreach, ZoomInfo, and Salesforce, and collaborate with account executives, sales engineers, channel partners, and marketing. The role includes booking discovery calls, creating opportunities, meeting funnel and pipeline KPIs, and closing transactional deals and upsells.

Lead GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.

Develop and productionize fraud detection machine learning models using large-scale behavioral, device, network, and transaction data. Lead investigations into complex fraud attacks, reconstruct attacker behavior, identify trends, and produce technical reports and customer-facing insights. Collaborate with product, engineering, operations, legal, executives, and customers to improve detection systems and automate analytic workflows.

Implements established machine learning, artificial intelligence, and statistical methods for large-scale neuroimaging research. Performs data preparation, quality control, feature extraction, multimodal data integration, workflow maintenance, troubleshooting, and computational support on local and high-performance systems. Modifies scripts, documents analytical procedures and outputs, and escalates complex issues. The role primarily supports the CentileBrain precision neuroscience project and may assign tasks to students.

The Applied Scientist will develop next-gen AI systems for autonomous driving, focusing on machine learning, simulation, and robotics, advancing embodied AI technologies.

Design, implement, test, deploy, and operate highly available distributed services for Azure Monitor at global scale. Build telemetry collection, routing, storage, and data-access APIs and tools; improve reliability, security, observability, and diagnostics; and integrate machine learning and AI-assisted operational capabilities. Participate in on-call and incident response, collaborate across teams, mentor engineers, lead design reviews, and drive architectural alignment.

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 property and casualty insurance pricing analyses, rate development, predictive modeling, portfolio evaluation, and regulatory filing support. Develops and enhances GLMs, machine learning models, rating structures, and automated analytical processes using SQL, Python, R, Excel, and Earnix. Provides pricing recommendations, technical guidance, peer review, mentoring, and stakeholder communication while ensuring compliance with actuarial standards and regulatory requirements.

Develop investment analytics that generate actionable insights and improve investment manager effectiveness. Responsibilities include researching analytic methodologies, building proofs of concept, leveraging financial datasets, analyzing performance and risk, productionalizing solutions, resolving complex issues, and applying AI to financial modeling, anomaly detection, and natural-language querying. The role collaborates with investment managers, senior leadership, and platform teams while communicating technical concepts and mitigating process risks.

Leads an Investment Insights team creating financial analytics that improve investment manager effectiveness. Responsibilities include designing and developing analytics, researching methodologies, partnering with investment teams and senior leadership, productionizing solutions, applying AI, resolving complex analytical issues, and managing analysts across Canada and India. The role also shapes team strategy, develops talent, manages risks, and builds stakeholder relationships.

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.

Develop and maintain behavior-planning systems for autonomous vehicles operating in structured, low-speed airport environments. Responsibilities include designing planning algorithms, implementing scalable C++ and Python software, modeling multi-agent interactions, testing in simulation and real-world scenarios, optimizing performance and latency, documenting system designs, and collaborating with autonomy and engineering teams.

Lead the Content Foundations engineering team and evolve Scribd’s high-scale document ingestion platform into a real-time, AI-native system. Responsibilities include setting technical strategy, integrating AI and ML capabilities, overseeing distributed data infrastructure, ensuring security and operational reliability, managing the roadmap, hiring and developing backend and AI data engineers, and partnering with product and engineering leaders across the organization.

Leads lab-in-the-loop biology programs that connect experimental design with machine learning model development. Oversees closed-loop experiments, model-output verification, perturbation studies, data generation, assay and model-system development, automation, metadata standards, CRO and vendor relationships, budgets, and cross-functional collaboration. Builds and mentors technical teams while aligning experimental and computational groups across a matrixed biotech organization.

Conduct fundamental AI research at the intersection of machine learning, physics, computational chemistry, and drug discovery. Build, optimize, and test generative and multimodal foundation models; run large-scale experiments; analyze results; implement research ideas in reliable code; and present findings to a multidisciplinary team.

Assist Product Designers and Engineers in creating user-centered digital product experiences. Responsibilities include developing wireframes, mockups, prototypes, and user flows; applying design patterns; auditing design consistency; participating in design reviews; and collaborating cross-functionally to launch usable advertising platform features.

Leads enterprise solution sales for Azure Databricks, advising executives, customers, partners, and technical teams on cloud, data, AI, and modernization strategies. Drives complex sales opportunities, usage targets, proof-of-concepts, solution demonstrations, competitive strategies, partner engagement, and deployment success. Acts as a subject matter expert and influences Microsoft go-to-market and product strategies.

Design and lead end-to-end architectures for production machine-learning systems at extraordinary scale. Responsibilities span data preparation, feature generation, model delivery, deployment, hosting, inference, monitoring, performance optimization, and technical design. The role establishes engineering standards, influences ML architecture, and collaborates across data science and software engineering to build reliable, maintainable systems for high-volume, low-latency workloads.

Design, develop, optimize, and deploy scalable computer vision and vision-language systems using classical computer vision and deep learning. Build evaluation frameworks, monitor production models, debug issues, and improve architectures with new data. Collaborate with product managers and cross-functional teams to deliver practical AI solutions across cloud and edge environments.

Designs benchmarks, reinforcement learning environments, evaluation methodologies, scoring frameworks, and reproducible evaluation architectures for frontier models. Builds and ships production code, maintains data pipelines, analyzes results, and collaborates with research scientists, solutions architects, and ML engineers. The role combines independent research methodology with hands-on engineering across agentic systems, model evaluation, and RL infrastructure.

Build and improve Firefox WebCompat internal dashboards, reporting tools, and broken-site screenshot capture pipelines. The role involves creating reliable data views for triage and metrics, automating screenshot workflows, collaborating with WebCompat and QA teams, and translating reporting needs into effective tooling. Familiarity with WebCompat workflows, Bugzilla, screenshot automation, and machine learning is preferred.

Design, implement, and deploy efficient adaptive machine learning systems that retrain or fine-tune models using production data. The role combines applied research with production engineering, including data strategy, model evaluation, online learning, reinforcement learning, observability, and real-world product development. The engineer will own data products, solve novel technical challenges, and collaborate closely with the founding team on research and product direction.

Lead and coach the engineering team building AutoReview, an AI-powered mechanical design review product. Partner with Product on roadmap planning, feasibility, estimation, prioritization, and timelines. Maintain technical context to identify risks and dependencies, guide initiatives involving DFM checks, assembly analysis, and bulk workflows, and collaborate with applied ML teams. Establish clear ownership, support engineer autonomy, and help the team navigate evolving customer needs and AI technologies.