Top Machine Learning Jobs in Vancouver

Design, implement, and monitor AI/ML models to improve underwriting risk assessment, pricing, and portfolio management. Translate underwriting rules into data models, integrate models into workflows, analyze datasets for risk patterns, build decision-support tools, ensure compliance and fairness, and collaborate with underwriting, actuarial, product, and engineering teams.

Contract role combining actuarial science, risk management, and AI to improve portfolio performance. Responsibilities include loss and combined ratio analysis, portfolio risk assessment, catastrophe and exposure modeling, stress testing, and integrating ML to enhance pricing, reserving, and risk selection while collaborating with underwriting, finance, and product teams.

As an Applied AI Engineer at Luminai, you will design experiments, optimize machine learning models, and enhance AI platforms for healthcare automation.

The Account Executive will drive sales, develop strategies for business growth, manage accounts, and lead collaborations with clients and teams in the insurance sector.

The Hyperscalers and Commercial Lead will develop technology-driven business strategies, drive customer adoption, and manage end-to-end sales processes while working closely with cross-functional teams.

Partner with multinational clients to gather requirements, define KPIs, develop business cases, and oversee GRAVTY® loyalty program implementations. Provide product expertise, deliver client training, manage project documentation and SLAs, collaborate with product management on features, and create product marketing collaterals to support go-to-market strategies.

Interns will prototype rapidly, iterate, and contribute to open-source projects at the intersection of AI and cryptography.

Conduct R&D and implement state-of-the-art NLP and ML algorithms for real-time, agentic voice and chat systems. Build and deploy LLM-based, multilingual and multimodal features, manage large text datasets, evaluate model performance, collaborate with engineering and product teams, and publish research.

Lead development and productionization of fraud prediction models (tabular, graph, behavioral). Build scalable feature pipelines, run experiments, integrate models into real-time/batch systems, monitor model and data health, and collaborate cross-functionally to maintain model reliability and performance.

Lead design and implementation of high-throughput, low-latency programmatic bidding systems. Collaborate with ML engineers to deploy real-time inference, optimize distributed microservices, run experiments and quantitative analysis, and partner with product to ship scalable features. Foster operational excellence, mentor engineers, and continuously improve performance and reliability.

Analyze and optimize payment-risk rules, thresholds, customer segments, and manual-review strategies. Use SQL, Python, experimentation, and decision-science techniques to reduce false positives, customer friction, operational workload, and payment losses. Investigate fraud patterns, build monitoring and reporting, evaluate risk changes, and provide practical recommendations while partnering with Risk Operations, Product, Engineering, and Data teams.

As a Staff Machine Learning Engineer, you'll lead AI initiatives using large-scale data, optimize ML models for edge devices, and collaborate with cross-functional teams.

Develop and evaluate machine learning and generative AI models using linked genomic and clinical data. Build LLM agentic systems, clinical variant interpretation pipelines, genomic representations, and reliable evaluation workflows. Investigate healthcare data quality, present findings, collaborate with bioinformatics and clinical teams, and contribute to scientific publications. The role requires strong Python, statistics, experimentation, cloud GPU, and software engineering practices, plus interest in biology and precision medicine.

Lead cross-functional teams of machine learning engineers and data scientists, driving technical direction and fostering a collaborative environment to develop high-quality ML-driven capabilities.

Lead the Platform Software Engineering team building and scaling core data infrastructure for research, market-making, and risk platforms. Hands-on role combining individual contributor work, system architecture, cross-functional project leadership, and people management while collaborating with Research and Business Operations.

The Forward Deployed Research Scientist engages directly with AI labs to develop data strategies, fine-tune models, run experiments, and collaborate on publishing research, all in a fast-paced client-focused setting.

Build and maintain backend APIs and data pipelines that ingest and process large-scale construction data (360° video, schedules, IoT). Optimize storage and query performance using DuckDB and PostgreSQL, extend Airflow workflows, integrate Wasm for local computation, monitor data quality, and collaborate with frontend, CV, and ML teams to deploy production models and analytics.

Build infrastructure and software for training, deploying, scaling, monitoring, and operating machine-learning models and optimization algorithms for supply-chain applications. Responsibilities include translating supply-chain use cases into mathematical models, developing ML and optimization systems in C++ and Python, designing automated testing strategies, and supporting cloud or containerized workloads. The role also involves research into heuristics, optimization, GPU computing, MLOps, and AI-assisted software development.

Serve as the primary technical advisor for SMB sales opportunities, partnering with Account Executives throughout sales cycles. Lead technical discovery, product demonstrations, proof-of-value engagements, competitive evaluations, security questionnaires, RFPs, and technical documentation. Advise IT, security, developer, DevOps, and executive stakeholders on identity, endpoint, SaaS, secrets, cloud, and AI security solutions. Collaborate cross-functionally to improve demos, playbooks, enablement, and customer education.

Build large-scale machine learning systems for advertising, including ranking, recommendation, targeting, prediction, optimization, and creative generation. Develop feedback loops connecting generative AI outputs to advertising performance signals, apply prompt engineering and post-training methods, run experiments, and ship reliable production ML systems. Collaborate across Product, Research, Engineering, and go-to-market teams to deliver AI-native advertising products.

Designs and operates scalable ML infrastructure, including training, serving, inference, orchestration, and deployment pipelines. Optimizes GPU and CPU workloads, manages cloud clusters, implements observability and SLOs, and secures automated CI/CD and infrastructure-as-code workflows. Partners with research and AI engineering teams to productionize models, while building reusable tooling, templates, and runbooks. The role also supports low-latency model serving, data quality, model promotion, and cost optimization.

Develop and deploy production machine learning products for financial workflow automation. Responsibilities include designing ML features, training models, building APIs and scalable services, evaluating model performance, ensuring observability, and applying LLMs, RAG, natural language understanding, and autonomous agents to business documents and workflows. The role also requires technical leadership and mentoring junior engineers.

Develop and optimize large-scale advertising ranking models for CTR and CVR prediction, calibration, user representations, and sequence modeling. Own machine learning systems end to end, including data pipelines, feature engineering, training, evaluation, deployment, and maintenance. Collaborate with platform, product, data science, and engineering teams to run experiments and improve advertiser outcomes, revenue, and user relevance. Apply deep learning and recommendation techniques within production latency, reliability, and cost constraints.

Conduct machine learning research operations using existing infrastructure. Evaluate neural network quality, failure modes, and edge cases; coordinate experiments with researchers; manage concurrent projects and deadlines; review results; recommend theoretical and process improvements; and prepare technical reports communicating qualitative and quantitative findings to external audiences.

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.