Top Machine Learning Jobs in Vancouver

Lead assessment, redesign, and hands-on implementation of data pipelines, ML workflows, and platform infrastructure. Produce prioritized system designs, harden production pipelines, improve observability, reliability, and data quality, and mentor the data team.

Leads the architecture, development, optimization, deployment, and lifecycle management of enterprise machine learning systems. Establishes AI engineering standards, MLOps practices, data quality processes, model governance, security, and validation frameworks. Provides technical leadership, mentors engineers, evaluates emerging technologies, and supports AI transformation initiatives while partnering with data scientists, engineering, product, and business teams.

Develop, deploy, and optimize scalable machine learning solutions, including predictive, deep learning, and intelligent automation models. Build data pipelines, prepare large datasets, monitor production models, manage retraining, and ensure quality, security, compliance, and responsible AI practices. Collaborate with data scientists, engineers, product teams, and stakeholders while researching emerging AI technologies and improving AI architectures.

Build and ship end-to-end ML/AI systems (traditional ML, NLP, LLMs/AI agents) on transaction, merchant, and location data. Prototype, productionize, and monitor models, collaborate with engineering and product, and (for senior hires) shape roadmap and mentor teammates.

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

The specialist will enhance TRIUMF's research through Machine Learning and Quantum Computing, mentor students, lead software development for high-performance computing, and foster partnerships in academia and industry.

Lead architecture and hands-on development of high-performance cloud and on‑prem streaming platforms. Drive technical vision, mentor teams, ensure scalability, low-latency media delivery, observability, CI/CD, and integration of machine-learning pipelines into production systems.

Lead R&D on quantum-classical hybrid algorithms and system benchmarking. Design and run experiments, reproduce literature results, analyze QPU contributions, communicate findings, and mentor teammates to improve performance and scalability.

As a Senior/Staff Machine Learning Engineer, you will design scalable data pipelines, implement ML algorithms, and collaborate with engineers and Data Scientists to optimize the digital advertising platform.

Lead enterprise data, analytics, AI, automation, and information governance. Define strategy and roadmap, modernize data platforms (lakehouse/Azure Databricks), establish AI and automation capabilities, enforce governance/MDM (Semarchy), deliver BI/Power BI and predictive solutions, run Automation CoE, drive data literacy, and build a high-performing cross-functional team.

Independently validate and monitor machine learning models used for credit underwriting, credit risk, and fraud detection. Identify weaknesses, recommend improvements, and collaborate with model owners to remediate findings. Partner cross-functionally to implement and maintain the Model Risk Management framework and support audit, compliance, and regulatory requests.

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

Own end-to-end analysis of payments, chargebacks, and user lifecycle to identify drivers of net loss. Deep-dive into fraud cases, design and evaluate A/B experiments and risk-rule changes, build monitoring dashboards, partner with Risk, Payments, and Product to convert insights into mitigations, and run ad-hoc analyses of emerging loss trends.

Develop data-analysis pipelines bridging ultrasound, synthetic biology, imaging and sequencing. Collaborate with wet-lab and ML teams to define objectives, encode priors, run ML pipelines for de-novo design and closed-loop active learning, and produce actionable insights to accelerate molecule discovery and device optimization.

Design and build distributed backend systems, microservices, and near-real-time data ingestion and processing. Scale an incrementality experimentation platform, add AI-powered workflows, extend data integrations and partner exports, and contribute frontend components as needed to deliver end-to-end product features.

As a Founding AI Engineer, you will develop an AI-powered knowledge platform, focusing on backend systems, machine learning data pipelines, and optimizing tech stack for performance while participating in the entire product lifecycle.

Lead and scale Mimica's product organisation, define global product strategy and roadmap for AI-driven task/process mining and automation, mentor product and design teams, improve product operations and discovery, align cross-functional stakeholders, handle escalations, and hire and forecast team needs.

As a Senior Impact Analyst, you will analyze sustainability challenges, design and manage interventions, and work closely with smallholder communities, emphasizing rigorous research and collaboration.

The Senior B2B Marketer will develop and execute marketing strategies, collaborate with Sales, create marketing materials, conduct research, and optimize marketing effectiveness to drive growth in the gaming and 3D industries.

Support P&C pricing, underwriting and product development through data extraction, cleaning, actuarial analyses, rating model development, trend and loss ratio studies, and documentation. Collaborate with senior actuaries on research, predictive modeling, and decision support while progressing through CAS exams.

Design and develop scalable, high-performance data and API infrastructure for real-time processing. Mentor engineers and collaborate with teams to enhance AI model evaluations.

Responsible for designing experiments to evaluate AI models, developing evaluation methodologies, analyzing performance data, and collaborating on production insights.

Designs, builds, and evaluates language models for AI security products. Responsibilities include dataset curation, preprocessing, LLM experimentation, safety and robustness evaluation, dashboards, knowledge graphs, model fine-tuning, production integration, monitoring, CI/CD, and ML tooling. Collaborates with researchers and data scientists to automate quality assurance and guardrails while delivering reliable, scalable, and interpretable models.

Build and iterate underwriting ML models for real-time transaction decisions. Develop feature pipelines and training datasets, prototype and evaluate models, productionize into batch or real-time systems, and instrument model/data health, retraining, and monitoring. Collaborate with engineering, risk, product, and ML platform teams to deploy robust, low-latency solutions with appropriate risk controls.

The CTO will lead Fathom's technical strategy, focusing on AI governance, building partnerships, defining standards, and advancing AI safety practices.