Top Machine Learning Jobs in Vancouver

Conduct AI/ML literature and technology trend research, produce technology reports, evaluate and recommend cutting-edge AI/ML methods, collaborate with cross-functional teams to plan and implement AI projects, and promote innovation and continuous improvement within the AI/ML team.

Support design, development, deployment, and maintenance of AI/ML solutions including LLMs and Agentic AI. Build and test deep learning and traditional models, prepare data, develop pipelines, deploy on cloud (prefer Azure/GCP), collaborate with cross-functional teams, and follow security, privacy, and responsible AI practices.

The Senior Product Manager will set product direction, own the development lifecycle, collaborate with teams, and ensure high-quality outputs. This role requires strong opinions on product quality and customer insights.

The Applied Scientist will drive research on efficient adaptive machine learning, deploy ML systems end-to-end, and contribute to product strategy, focusing on real-world impact and innovation.

The Fractional CTO will lead AI-focused projects, manage technical teams, and guide startups in technology development and strategic planning.

Design, build, and integrate AI-powered features using foundation models and managed cloud AI services. Develop RAG pipelines, agent/tool-calling workflows, fine-tune hosted models, implement data governance and security, evaluate vendors, establish monitoring and cost-control, define technical standards, mentor developers, and work with risk, compliance, and business stakeholders to deliver regulated AI solutions.

Lead and execute product strategy for Everyday Money: define product thesis and roadmap, ship product lines, run AI innovation lab for agentic consumer experiences, manage and develop PMs, partner with design, Credit, and external partners, and own business economics and compliance for durable retention and monetization.

Develop and deploy generative AI and LLM solutions end-to-end for e-commerce: research models, build ETL/data pipelines, run distributed training and experiments, collaborate with product and engineering to deliver production systems that improve recommendations, search, personalization, and customer interactions.

Drive independent, high-impact research on frontier AI topics, publish findings, collaborate widely, and mentor new researchers in the field.

Design, build, and operate ML infrastructure and scalable training/serving pipelines. Manage GPU clusters, cloud resources, CI/CD, IaC, SLOs, observability, security, and cost optimization. Collaborate with research teams to productionize models and create runbooks, templates, and tooling for repeatable ML workflows.

Build and maintain responsive Angular UI components, collaborate with UX and backend teams, implement REST APIs and Python backend scripts, integrate basic ML models into data workflows, and participate in the full SDLC within Agile teams while ensuring code quality and performance.

The Software Engineer, Perception develops autonomous aircraft systems to detect and manage wildfires using real-time data processing and machine learning techniques.

The Lead Metallurgist will oversee resource evaluation, audit geometallurgy models, liaise with teams, ensure quality control, and mentor junior staff.

Own a portfolio of large manufacturing accounts, drive net-new pipeline and full-cycle enterprise deals, build C-suite relationships, translate AI/vision capabilities into ROI, execute land-and-expand strategies, and travel 40-50% for on-site engagement and executive reviews.

Provide legal support for commercialization and product development of an AI-powered ad tech platform: draft and negotiate commercial agreements, manage IP portfolio and complex IP litigation, advise on AI, data, and privacy matters, counsel product teams on compliance and IP protection, and develop automated legal workflows.

Lead development of ML components for medical image reconstruction: define training/evaluation pipelines and metrics, productionize models (inference performance, monitoring, safe fallbacks), collaborate on hybrid physics-ML algorithms, and build tooling for experimentation and rigorous verification.

Design, build, train, and deploy production ML models and scalable pipelines. Implement MLOps practices, observability, governance, and lineage. Optimize model performance and costs, collaborate with product, risk, and engineering teams to integrate ML features into production systems.

Lead and evolve a centralized QA program for Customer Support, defining frameworks, scorecards, and calibration practices. Use AI-enabled QA tooling to analyze omnichannel interactions, surface insights, drive coaching and operational improvements, partner cross-functionally, and potentially manage QA specialists as the function scales.

Senior pre-sales engineer partnering with sales and channel partners to run technical demos, lead POCs, design solutions, and close large deals. Present to executives and technical audiences, build cloud ML/generative AI solutions, and advise on data architecture and Snowflake positioning.

Support Data Science and Regulatory Affairs to define and execute regulatory strategies for AI/ML-enabled medical devices. Translate ML concepts into regulatory documentation, create DHF and submission materials (510(k), EU MDR, Health Canada), maintain dataset and model documentation, and manage version control and change management.

Develop prototypes, PoCs, and MVPs for GenAI solutions. Apply deep learning and ML principles using Python, Hugging Face, LangChain, OpenAI API, TensorFlow/Keras/PyTorch, and cloud model services. Work with multi-modal data and intelligent agent tools. Be self-motivated, collaborative, and focused on solving hard AI/GenAI problems.

Lead Android-first development of growth experiments and features, using AI-native workflows to design, implement, instrument, and analyze A/B tests. Ship performant, reliable Android experiences at scale, mentor teammates, participate in on-call, and codify AI-native engineering playbooks for broader adoption.

As an AI/ML Engineer, you will design and deploy models for real-world applications, optimize data pipelines, and engage with global leaders to enhance AI technology's impact across sectors like healthcare and government.

Build and automate large-scale ML data pipelines and tooling for autonomous trucking. Improve dataset curation, deduplication, labeling, evaluation, and metrics infrastructure. Collaborate with robotics, autonomy, and infra teams to support continuous learning, deployment, and scalable AI infrastructure for Kodiak's fleet.

The Machine Learning Engineer will develop and deploy ML models, leveraging analytical skills to automate processes and reveal data value.