Top Machine Learning Jobs in Vancouver

The Cloud Inference Engineer is responsible for building and maintaining a scalable LLM inference platform, focusing on distributed systems and machine learning technologies while collaborating across teams to optimize performance.

Build, optimize, and embed machine learning models for on-device inference within a Go-based detection engine. Own models end-to-end: deploy, monitor performance, detect drift, iterate, and resolve production issues while collaborating with systems engineers and stakeholders.

The Technical Product Manager will lead product direction, interface with engineering teams, and ensure the successful deployment of AI-powered safety systems. Responsibilities include defining product vision, translating customer needs, and maintaining high product quality.

Lead sensitivity studies and simulation development for the XLZD liquid xenon observatory, applying Monte Carlo and machine learning techniques for 0νββ searches. Support calibration and detector R&D at TRIUMF, produce technical reports and publications, present results, and mentor students within an international collaboration.

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

Lead the development of fraud prediction models, build feature pipelines, prototype modeling ideas, and productionize models while collaborating with cross-functional teams.

Provide embedded legal counsel to product and engineering on privacy, IP, and consumer protection; lead Arena's global privacy program (GDPR, CCPA/CPRA, cross-border transfers); advise on AI governance and responsible deployment; support commercial contracts and data flow frameworks; build privacy processes, playbooks, internal agents, and legal-tech integrations to scale operations.

Design, build, and operate production ML decision systems to detect and prevent payment fraud, account takeover, scams, and other abuse. Integrate diverse signals into low-latency serving and batch scoring, own feature pipelines and model lifecycle, develop AI-assisted triage and feedback loops, and partner cross-functionally to balance fraud reduction with legitimate customer access.

Design, build, and operate production ML systems that generate trusted signals for ranking, retrieval, recommendations, propensity/churn/LTV, and next-best-action decisioning. Define signal/data contracts, own feature and candidate generation through serving, experimentation, monitoring, and feedback loops, and evaluate long-term business impact, trust, fairness, and compliance. Partner across product, data, modeling, risk, and compliance and apply AI/agents to accelerate engineering and operations.

Lead machine learning initiatives, oversee product development, improve execution pace, enhance team reliability, and foster team growth at Hightouch.

Design, build, and maintain scalable AI/ML pipelines for data processing, model training, and deployment. Collaborate with data scientists and engineers to implement and optimize AI solutions, troubleshoot pipeline issues, document best practices, and develop MVP code to transition into production-ready systems.

As a Research Engineer, you will develop and enhance AI algorithms for self-driving vehicles, collaborating with scientists to advance technology and support deployment in production systems.

Lead Narvar's Promise team of frontend, backend, and data science engineers to deliver AI-driven delivery date predictions. Drive roadmap, set technical direction, maintain quality, hire and grow staff, run hypothesis-driven cycles, and collaborate cross-functionally. Stay hands-on with AI/ML, cloud infrastructure, and engineering practices to improve SDLC and product conversion.

Manage and grow strategic partnerships across US & Canada for a Google Cloud-focused AI and data consultancy. Drive co-sell initiatives, GTM planning, partner enablement, joint marketing, event execution, partner onboarding, and pipeline generation while tracking partner performance and reporting impact.

Manage the vision, strategy, and roadmap for Contract Intelligence AI products. Collaborate with teams to define requirements and market strategies, ensuring delivery of generative AI solutions.

Lead end-to-end product design for Clio's Fintech/payments area: conduct customer research, frame opportunities, prototype and ship AI-enabled workflows, improve design systems, measure impact, and mentor other designers.

Lead development of AI-powered GTM tooling and infrastructure to drive pipeline, conversion, activation, retention, and revenue. Own metrics, run experimentation, partner with Sales/Marketing/Engineering/RevOps, and deliver scalable systems and revenue-compounding platforms.

Own the technical side of deal progression for airline deployments: qualify opportunities, run demos and POCs, author integration and deployment architectures (PSS, APIs, data feeds, DNS, identity, networking), respond to RFPs/security questionnaires, scope SOWs and NFRs, and build reusable demo/reference assets to drive adoption and expansion while liaising between sales, engineering, and customers.

Lead end-to-end personalization ML initiatives: build scalable ML pipelines, design CI/CD for models, monitor production performance, implement retraining and drift detection, partner with cross-functional teams, and mentor engineers.

This role involves leading the development of AI systems to replace lab toxicity experiments, focusing on end-to-end ML architecture, research on chemistry-biology correlations, and building innovative models. Candidates should have strong ML skills, engineering abilities in Pytorch and Python, and a passion for both science and technology.

As a founding Data Scientist/Machine Learning Engineer, you'll develop AI/ML models, enhance product capability, and drive impactful user outcomes while working closely with product teams.

Lead statistical activities for clinical trials from protocol design through reporting. Provide statistical oversight, collaborate with cross-functional teams and CROs, apply SAS/R (and ML) for analyses, automate processes, and mentor junior statisticians.

As a Senior Machine Learning Engineer, you'll design and develop AI systems, focusing on LLMs and improving agent performance while mentoring engineers.

Design, build, and operate scalable backend services in Python or Go to power underwriting decisioning. Integrate statistical and ML models, implement APIs (REST, gRPC/GraphQL), ensure observability and testing, lead projects, and collaborate with product, data science, and underwriting teams to automate underwriting workflows.

Lead technical vision, research, and engineering for a unified computer vision platform. Define long-term research agenda, drive MLOps and deployment across edge/cloud, set performance metrics, manage and scale distributed teams, and translate product needs into scalable, production-ready visual intelligence solutions.