Top Machine Learning Jobs in Calgary

Build trusted technical relationships with enterprise customers, troubleshoot Samsara hardware, software, APIs, and AI solutions, and optimize implementations. Serve as the primary technical contact, manage incident communications, identify growth opportunities, maintain account records, and collaborate across stakeholders. The role also supports Technical Account Management team development and requires occasional customer travel plus after-hours or weekend coverage when needed.

Build and improve Confluence AI experiences across frontend, backend, and AI integrations. Own features from design through launch, apply generative AI, retrieval, agents, and evaluation techniques, create automated quality testing, monitor production performance, run adoption experiments, and collaborate cross-functionally. Mentor engineers and contribute to architecture, observability, testing, safeguards, and scalable software delivery.

Leads AI transformation, governance, quality oversight, risk management, and responsible adoption across Pfizer Global Supply and QSEO. Establishes GxP-compliant AI lifecycle practices, inspection-readiness frameworks, governance forums, portfolio monitoring, inventory management, metrics, and reporting. Advises senior leaders on AI regulations, deployment risks, investment priorities, and compliance while leading cross-functional initiatives and influencing stakeholders across Quality, Digital, Operations, and R&D.

Leads GitLab’s Product Data Science team, setting its strategy, roadmap, analytical standards, and priorities. Partners with Product and Engineering leaders on experimentation, causal inference, analytics, and production modeling. Manages, hires, coaches, and retains data scientists while building a high-performing function. Provides hands-on technical review of models, analyses, and queries, and communicates insights and risks to senior leaders.

The Data Scientist will partner with Sales, Revenue Operations, and Finance to analyze GTM performance, build and productionize machine learning models, conduct experiments, forecast outcomes, and identify growth opportunities. Responsibilities include statistical analysis, causal inference, Python and SQL development, MLOps, agentic workflow automation, semantic-layer contributions, executive reporting, and communicating insights to nontechnical stakeholders in a fully remote environment.

Own company-critical technical problems and long-term strategy across multiple organizations. Architect scalable customer-facing systems and platforms, remain hands-on in software development, influence senior product and engineering leaders, establish engineering standards, and drive responsible AI adoption in products and workflows. The role requires deep expertise in large-scale software systems, consumer products, distributed systems, and production ML and LLM applications.

Own end-to-end analytical problems across risk, fraud, growth, and lending products. Develop strategies, monitor portfolio and macroeconomic trends, build metrics and analytical frameworks with Engineering and Machine Learning teams, and influence product, regulatory, compliance, and launch decisions. Engage external partners, translate complex insights into action, create AI-assisted workflows, and mentor junior analysts.

Lead Block’s global Workforce Optimization function across customer-facing and regulated operations. Establish strategy for forecasting, capacity planning, staffing, real-time management, analytics, automation, and vendor governance. Build predictive workforce systems, align operational and financial plans, manage risk and compliance capacity, oversee incidents and escalations, and develop a multidisciplinary global team supporting employees, outsourced partners, and AI systems.

Leads compliance technology oversight for transaction monitoring, sanctions screening, customer risk scoring, and AI-driven case resolution models. Builds AI-powered automation, governance frameworks, human-in-the-loop controls, real-time monitoring dashboards, data-quality controls, and model performance processes. Partners with engineering, machine learning, policy, regulators, and independent monitors to ensure models and data pipelines are explainable, auditable, exam-ready, and aligned with AML requirements across fiat and virtual currency products.

Develop and scale production machine learning models for credit decisioning, including default prediction and approval decisions. Build training, decisioning, monitoring systems, data pipelines, features, and algorithms. Conduct research, develop prototypes, and collaborate with engineering, credit, and product teams to deliver machine learning products from inception through business impact.

Develop and deploy advanced machine learning, LLM, and agentic solutions using structured and unstructured data. Build retrieval, tool-use, orchestration, and evaluation systems that are reliable and safe in production. Collaborate with engineering, product, operations, and data science teams to create ML-driven SaaS features. The role also involves agile development, cross-functional communication, customer engagement, mentoring, and continuous learning.

Own the direction and implementation of Babylist’s personalization domain, including recommendation, search, feed models, custom embeddings, and shared representations. Take ambiguous problems from concept through production, owning architecture, deployment, monitoring, retraining, evaluation, and customer impact. Partner with product, design, and data, establish AI development standards, make cross-team technical decisions, and coach senior engineers while remaining hands-on with complex machine learning systems.

Manages strategy, discovery, roadmap development, prioritization, delivery, adoption, and continuous improvement for an AI-enabled digital workflow product. Translates customer and business needs into product capabilities, collaborates with UX, engineering, data, and AI teams, defines requirements and success metrics, validates concepts, supports launches, and drives measurable user and business outcomes.

Leads a team of ML engineers developing and deploying agentic systems, LLM solutions, traditional machine learning models, retrieval pipelines, tool-calling workflows, and evaluation frameworks. The role guides technical design, code reviews, model and vendor evaluations, proof-of-concept development, cross-functional collaboration, mentorship, and production problem-solving across distributed engineering teams.

Develop and productionize fraud detection machine learning models using large-scale behavioral, device, network, and transaction data. Lead investigations into complex fraud attacks, reconstruct attacker behavior, identify trends, and produce technical reports and customer-facing insights. Collaborate with product, engineering, operations, legal, executives, and customers to improve detection systems and automate analytic workflows.

Analyze investment risk outputs, identify patterns and anomalies, and develop actionable insights. Support risk metric production, reporting, workflow monitoring, data-quality improvements, and automation of analytics processes. Collaborate with Business Technology teams and external vendors, document methodologies, and help enhance scalable risk-management capabilities using quantitative analysis, programming, and emerging technologies.

Build a functional prototype addressing a real RBC business challenge within a collaborative summer innovation program. Responsibilities include data transformation, engineering, cleaning, mining, product design, troubleshooting, development, stakeholder collaboration, research, strategy, communications, and executive presentations. Participants work with mentors and subject matter experts while applying data science, programming, agile development, and design-thinking techniques.

Own analytical relationships with travel technology partners, create scalable reporting and dashboards, conduct exploratory analysis, apply machine learning and statistical methods, and translate findings into recommendations. Build automated and agentic workflows using LLM tools, collaborate with product and engineering, and present clear insights to commercial and executive audiences.

Leads multi-track digital programs and portfolios, directing project managers, delivery plans, budgets, risks, resources, and client relationships. Establishes AI-enabled delivery guardrails, human review processes, and measurable value metrics. Coaches teams across Agile, Kanban, Waterfall, and agentic methods; contributes to proposals, pitches, and portfolio growth. Requires strong commercial judgment, enterprise digital delivery experience, senior stakeholder management, and hands-on fluency with AI tools and modern software delivery workflows.

Deploys and customizes Palantir Foundry and AIP solutions for enterprise clients across multiple industries. Responsibilities include conducting client workshops, building data pipelines and operational workflows with Python and TypeScript, solving complex technical problems, training users, documenting solutions, and supporting project delivery. The role requires strong data engineering skills, client communication, analytical problem-solving, and approximately 5% travel in Canada and the US.

Owns the strategy and roadmap for semantic insurance data products supporting AI, analytics, automation, and enterprise applications. Defines canonical concepts, relationships, metadata, data quality standards, APIs, knowledge graphs, and AI-ready consumption patterns. Partners with engineering, architecture, data, AI, UX, customer success, and insurance stakeholders to translate complex P&C insurance needs into product capabilities, prioritize use cases, establish requirements, drive adoption, and measure business impact.

Develop machine learning models and detection rules for web security products. Analyze large datasets to distinguish legitimate from fraudulent traffic, improve detection accuracy, support production data workflows, troubleshoot distributed systems, and provide insights that guide product improvements and strategy.

Lead a globally distributed endpoint security engineering team developing visibility, prevention, and machine learning capabilities across Windows, macOS, and Linux. Own the technical roadmap, delivery, cross-platform protection parity, and machine learning direction. Mentor senior engineers, manage hiring and career development, collaborate with product and engineering teams, maintain platform vendor relationships, and represent the team through customer engagement, conference presentations, and technical research.

Leads and scales Jobber’s credit risk function across payments and lending. Responsibilities include managing credit risk analysts, setting risk appetite and policy, overseeing underwriting, monitoring, collections, forecasting, provisioning, stress testing, and model governance. Partners with Data Science, Finance, Legal, Compliance, Product, and funding partners to build AI-enabled decisioning, automate operations, meet Canadian and U.S. requirements, manage vendors, and report portfolio performance to executives and risk committees.

Designs end-to-end architectures and high-level designs for web security products and features, spanning configuration, reporting, runtime systems, analytics, and attack detection. Collaborates with product management and cross-functional teams to define requirements, guides engineering implementation, presents designs, and contributes security product expertise and technical roadmap direction.