Top Machine Learning Jobs in Calgary

Lead engineering strategy and execution for GitLab's growth and monetization platforms including self-service purchase, billing, usage metering, entitlements, quote-to-cash, integrations (Zuora, Salesforce), and API-first scalable architectures. Align cross-functional partners, ensure reliability, compliance (SOX), and apply AI/ML for predictive pricing and usage forecasting to support usage- and impact-based pricing.

Lead product strategy for consumer credit, underwriting, merchant pricing, and loan economics. Define features, specs, and metrics; partner with engineering, analytics, design, commercial, legal, and capital to launch and scale products across channels and geographies.

As a Staff Machine Learning Engineer, you will enhance credit modeling, manage full credit modeling stacks, and support innovations within regulated banking frameworks.

Manage a team of ML engineers to develop machine learning solutions for underwriting applications. Set technical strategy, mentor engineers, and collaborate with teams across the organization.

As a Staff ML Engineer, you'll design and operate Samsara's ML platform, partnering with teams to deliver scalable ML solutions that enhance safety and efficiency in physical operations.

Lead cross-organizational technical strategy and execution for core sharing and collaboration systems. Architect and implement foundational frontend/backend and platform solutions at massive scale, drive AI/LLM adoption, mentor engineering leaders, resolve systemic bottlenecks, and remain hands-on in critical design, prototyping, and code contributions.

Own vision, strategy, and roadmap for cost-optimization and energy-management products for fleets. Translate telematics and operational data into customer-facing features, run discovery, prototype MVPs, define success metrics, and ship end-to-end with cross-functional teams.

Lead end-to-end growth marketing for 1Password's SaaS Manager: build demand gen and cross-sell programs, run experiments, partner with Product/PMM/Sales, leverage AI agents and automation to scale pipeline and create repeatable growth playbooks.

Lead a team of ML engineers to develop and improve fraud detection models, defining strategies and fostering collaboration across functions to ensure effective model integration.

Lead end-to-end product design for real-time insights, reporting, and analytics in a legal SaaS product. Translate complex data and domain logic into scalable, accessible UI patterns, prototype AI-powered features, conduct customer research, and partner with product and engineering to drive strategy, launches, and iterative improvements.

Lead delivery of ML feature platform initiatives: design, build, and operate backend systems for feature creation, storage, backfilling and serving. Collaborate with product, design, and analytics, own quarterly goals, improve platform reliability and code quality, mentor engineers, and support on-call and operational monitoring.

Design and implement scalable data pipelines and ML algorithms to optimize StackAdapt's digital advertising platform. Collaborate with teams to solve complex problems.

Design, deploy, and maintain end-to-end ML-driven risk solutions at scale to detect and prevent fraud, abuse, and credit risk. Lead technical decisions, build ML tooling and processes, apply state-of-the-art models and third-party data, investigate emerging risk patterns, and collaborate with platform and cross-functional teams to ensure reliable real-time model operation.

Build and maintain scalable, reliable ML data platform systems to support dataset generation, model training, analytics, monitoring, and large-scale inference. Collaborate with ML, software, and infrastructure engineers to design cost-efficient data lake and training infrastructure using vendor and open-source tools to enable next-generation ML models.

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 product development for Maestro, an AI platform for mortgage origination and servicing. Translate operational workflows into PRDs, define metrics (throughput, time-to-decision, accuracy), collaborate with ML, engineering, underwriting ops, and stakeholders, build feedback loops between AI output and human judgment, and drive roadmap execution in a regulated, high-ambiguity environment.

Lead a major product area end-to-end: set strategy and KPIs, define customer personas and market thesis, prioritize features, drive execution with engineering, and align cross-functional stakeholders to deliver high-impact AI-enabled hiring and people-analytics solutions.

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.

Support and expand grid analytics by developing probabilistic engineering and market studies, building congestion and nodal market models, designing analytic tools, and delivering forecasting, visualization, and actionable insights to inform transmission system planning and operations.

Build, test, and maintain software across full-stack, backend, infrastructure, data pipelines, and ML infrastructure to support autonomous trucking. Develop web interfaces, backend services, APIs, and tooling; improve reliability, observability, CI/CD, and deployment; participate in code reviews, debugging, and cross-team collaboration while learning new technologies.

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.

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.

Enterprise sales role responsible for full sales cycle in Canada: prospecting, building executive relationships, selling Feedzai's AI-driven fraud and AML RiskOps platform, meeting quota, developing business cases, and aligning with GTM strategy to drive adoption and long-term customer relationships.

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.