Top Machine Learning Jobs in Calgary

Provide live-chat and email support to ecommerce partners using the AfterSell application. Troubleshoot technical issues, educate customers, resolve or escalate complex problems, collect product feedback, and contribute to projects such as documentation, partner onboarding, review generation, and ticket-triage improvements. The role also uses AI and automation to improve support efficiency, identify recurring issues, and develop scalable customer experience processes.

Lead technical implementation of a market management platform for nodal electricity markets. Design and operate large-scale, data-intensive production systems handling continuously changing data. Make architecture decisions, contribute production software, manage incidents, improve existing systems, and partner with product, engineering, data science, and power-market experts. Lead technical reviews, mentor engineers, and drive optimization, forecasting, and analytical capabilities.

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

Lead independent validation and ongoing monitoring of complex financial and analytics models, including ALM, loss forecasting, loan transition, corporate planning, and engagement models. Identify weaknesses, communicate findings, guide remediation, and strengthen model risk governance. Partner with Finance, Quantitative Research, Growth Analytics, Internal Audit, Internal Controls, Accounting, and Compliance on validation, audit, regulatory, and examiner matters.

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 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.

Performs hydraulic and flow assurance modeling for pipelines and energy systems, including steady-state and transient analyses. Develops studies, analyzes client data, documents technical findings, conducts regulatory compliance reviews, supports hydrogen and carbon capture projects, collaborates with engineering consultants, presents technical work, and contributes to business development. The role is based in Calgary under a hybrid work model.

Conduct user research and discovery, identify product friction, and translate findings into user stories, requirements, wireframes, and prototypes. Support product testing, feedback cycles, releases, client communications, and release notes. Use AI tools throughout the product development process while collaborating with product and engineering teams in a fast-paced, AI-driven environment.

Lead the underwriting and credit risk validation sector within Model Risk Management. Manage and mentor a quantitative team, perform hands-on validations of complex ML models, assess data integrity and model performance, identify and remediate weaknesses, and partner with stakeholders to enhance the enterprise MRM framework and meet audit and regulatory requirements.

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.

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, LTV, and next-best-action decisioning. Design reliable signal contracts covering freshness, provenance, confidence, eligibility, and calibration. Lead feature pipelines, model serving, experimentation, monitoring, and feedback loops while evaluating fairness, risk, compliance, trust, and long-term customer impact. Collaborate across product, growth, data, platform, modeling, risk, and compliance teams.

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, lifecycle intelligence, and next-best-action decisioning. Design reliable signal contracts with freshness, provenance, confidence, and calibration guarantees. Lead experimentation, monitoring, feedback loops, and impact evaluation focused on fairness, trust, risk, compliance, and long-term engagement. Partner across product, growth, data, platform, modeling, risk, and compliance teams.

Supports investment risk management by developing and monitoring risk metrics, reports, analytics workflows, and data processes. The intern analyzes risk outputs, identifies anomalies, automates reporting, improves data quality and operational efficiency, documents methodologies, and contributes to emerging technology applications. The role provides practical experience in quantitative analysis, programming, risk management, and institutional investment operations.

Provides enterprise-wide architectural leadership for data and AI, defining principles, standards, reference architectures, roadmaps, and governance guardrails. Advises executives and architecture teams on platform decisions, modernization, risk, privacy, security, regulatory alignment, and responsible AI adoption. Oversees major initiatives to improve consistency, reuse, data quality, and long-term sustainability across hybrid legacy and modern environments.

Own clinical quality for member-facing AI products by partnering with product, engineering, and clinical teams. Design clinical AI evaluations, define quality thresholds and rubrics, assess accuracy, omissions, hallucinations, medication-safety risks, and translate clinical reasoning into prompts and decision logic. Monitor evolving evidence and standards of care, validate product launches, and communicate findings to technical and clinical audiences.

Owns marketing analytics and measurement across campaigns, channels, attribution, funnel health, pipeline forecasting, and ROI. Builds dashboards, attribution models, AI-driven propensity scoring, forecasting, anomaly detection, and automated insights. Partners with marketing, sales, finance, data engineering, security, and legal to improve the marketing data stack, guide investment decisions, deliver executive reporting, and establish responsible AI standards. Mentors analysts and leads analytical practices across the marketing organization.

Lead strategy and delivery of AI-powered capabilities across Employ's recruiting platforms. Define product vision, prioritize roadmap, partner with engineering, design, and data science, ensure responsible AI practices, measure product success, and drive adoption to improve recruiter productivity and customer outcomes.

Review and modernize SAIT’s Industrial Instrumentation Certificate through comprehensive curriculum and market analysis. Identify outdated content, skill gaps, emerging technologies, appropriate software and simulation tools, and recommended courses or modules. Develop learning outcomes, practical activities, assessments, and training recommendations aligned with industrial instrumentation, automation, process control, AI, IIoT, safety, and workforce needs. Collaborate with colleagues and subject matter experts while managing deliverables, timelines, and stakeholder communication.

Updates machine learning training and test datasets, refines annotation guidelines, initiates model training, and evaluates model performance. The role annotates synthetic text and image data, conducts manual quality analysis, identifies error patterns, reports anomalies, and produces findings on utterance quality, LLM evaluation, ASR bugs, and customer pain points. It also applies basic NLP and linguistic knowledge to ensure data accuracy and consistency.

Serve as the technical SME for compliance automation, overseeing GRC control mapping, evidence collection, integrations, and continuous monitoring. Partner with engineering teams to embed compliance into workflows, support audits and framework rollouts, assign control ownership, reduce audit burden, and maintain audit readiness. Lead cross-functional initiatives and communicate compliance risks, control status, and progress to technical and leadership audiences. The role requires cybersecurity framework expertise, program management, cloud and API fluency, GRC platform experience, and familiarity with AI-enabled compliance workflows.

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.

Develop and maintain simulation scenes, implement motion-planning and collision-checking in ROS2/MoveIt2, build perception components for detection and point-cloud processing, write tested C++/Python code, debug software-hardware integration, and participate in code reviews, CI, and agile practices in onsite R&D/production facilities.

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.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

Own LanceDB’s product direction by shaping the roadmap with technical leadership, researching users and competitors, gathering continuous user insight, and driving cross-functional prioritization. The role partners closely with engineering as a technically respected peer, defines product changes for AI and data-platform users, uses metrics and experiments to guide decisions, and operates with high ownership in an ambiguous environment.