Top Machine Learning Jobs in Calgary

Develop data science solutions for payments across Square and Cash App. Build AI-powered tools, ETL pipelines, dashboards, machine learning models, experiments, and cloud data systems. Partner with Product, Engineering, Finance, and Operations to improve payment success, cost efficiency, risk mitigation, and decision-making. Translate business needs into practical solutions, research questions independently, and operationalize data processes and infrastructure.

Develop data tools, AI automations, ETL pipelines, dashboards, and machine learning models for payments. Analyze experiments, optimize payment performance and costs, mitigate risk, maintain data systems and cloud infrastructure, and translate business needs into actionable solutions while partnering with Product, Engineering, Finance, and Operations.

Build and operate scalable full-stack software and AI-powered products for IoT-driven physical operations. Responsibilities include defining technical strategy, developing backend services, APIs, frontend and potentially mobile features, transforming AI capabilities into customer products, partnering across functions, leading complex projects, mentoring engineers, and improving engineering standards.

Owns analytics and optimization strategy for advertising revenue across Zynga’s mobile game portfolio. Responsibilities include monitoring performance, diagnosing trends, designing A/B tests, identifying monetization opportunities, partnering with product, analytics, and data science teams, supporting AI and ML models, leading cross-functional initiatives, and mentoring analysts.

Drive pipeline and revenue growth through digital prospecting, lead qualification, relationship building, and SaaS sales. Engage prospective enterprise customers, manage inbound and outbound leads, use tools such as LinkedIn, 6Sense, Outreach, ZoomInfo, and Salesforce, and collaborate with account executives, sales engineers, channel partners, and marketing. The role includes booking discovery calls, creating opportunities, meeting funnel and pipeline KPIs, and closing transactional deals and upsells.

Lead GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.

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.

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.

Design scalable human-data solutions for frontier AI clients, translating ambiguous model-training and evaluation needs into executable programs. Lead discovery, methodology, task and workflow design, feasibility estimates, expert profiling, quality strategies, and technical architecture in partnership with engineering and project teams. Advise on fine-tuning, RLHF, benchmarks, red teaming, multimodal data, coding tasks, and agent environments while guiding pilots through early delivery and developing reusable best practices.

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.

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.

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.

Designs and implements enterprise cloud and AI solutions for unstructured data workloads. Responsibilities include building ingestion pipelines, API integrations, document extraction and HITL workflows, tuning machine learning models, benchmarking performance, and ensuring security, availability, disaster recovery, and data isolation. The role also supports customer developers, leads technical proofs of concept, mentors engineering teams, develops reusable blueprints, and provides feedback to product engineering.

Conduct novel research in generative and predictive AI for molecular systems. Lead a research project from ideation through experimentation, develop methods involving foundation models, diffusion models, large language models, reinforcement learning, or multimodal learning, run large-scale experiments, evaluate hypotheses, and present findings. The role includes mentorship and potential publication opportunities while contributing to AI-driven drug discovery.

Leads lab-in-the-loop biology programs that connect experimental design with machine learning model development. Oversees closed-loop experiments, model-output verification, perturbation studies, data generation, assay and model-system development, automation, metadata standards, CRO and vendor relationships, budgets, and cross-functional collaboration. Builds and mentors technical teams while aligning experimental and computational groups across a matrixed biotech organization.

Conduct fundamental AI research at the intersection of machine learning, physics, computational chemistry, and drug discovery. Build, optimize, and test generative and multimodal foundation models; run large-scale experiments; analyze results; implement research ideas in reliable code; and present findings to a multidisciplinary team.

Assist Product Designers and Engineers in creating user-centered digital product experiences. Responsibilities include developing wireframes, mockups, prototypes, and user flows; applying design patterns; auditing design consistency; participating in design reviews; and collaborating cross-functionally to launch usable advertising platform features.

Leads enterprise solution sales for Azure Databricks, advising executives, customers, partners, and technical teams on cloud, data, AI, and modernization strategies. Drives complex sales opportunities, usage targets, proof-of-concepts, solution demonstrations, competitive strategies, partner engagement, and deployment success. Acts as a subject matter expert and influences Microsoft go-to-market and product strategies.

Design and lead end-to-end architectures for production machine-learning systems at extraordinary scale. Responsibilities span data preparation, feature generation, model delivery, deployment, hosting, inference, monitoring, performance optimization, and technical design. The role establishes engineering standards, influences ML architecture, and collaborates across data science and software engineering to build reliable, maintainable systems for high-volume, low-latency workloads.

Design, develop, optimize, and deploy scalable computer vision and vision-language systems using classical computer vision and deep learning. Build evaluation frameworks, monitor production models, debug issues, and improve architectures with new data. Collaborate with product managers and cross-functional teams to deliver practical AI solutions across cloud and edge environments.

Designs benchmarks, reinforcement learning environments, evaluation methodologies, scoring frameworks, and reproducible evaluation architectures for frontier models. Builds and ships production code, maintains data pipelines, analyzes results, and collaborates with research scientists, solutions architects, and ML engineers. The role combines independent research methodology with hands-on engineering across agentic systems, model evaluation, and RL infrastructure.

Build and improve Firefox WebCompat internal dashboards, reporting tools, and broken-site screenshot capture pipelines. The role involves creating reliable data views for triage and metrics, automating screenshot workflows, collaborating with WebCompat and QA teams, and translating reporting needs into effective tooling. Familiarity with WebCompat workflows, Bugzilla, screenshot automation, and machine learning is preferred.

Lead and coach the engineering team building AutoReview, an AI-powered mechanical design review product. Partner with Product on roadmap planning, feasibility, estimation, prioritization, and timelines. Maintain technical context to identify risks and dependencies, guide initiatives involving DFM checks, assembly analysis, and bulk workflows, and collaborate with applied ML teams. Establish clear ownership, support engineer autonomy, and help the team navigate evolving customer needs and AI technologies.

Owns marketing measurement across campaigns, channels, attribution, funnel health, and pipeline forecasting. Builds dashboards, attribution models, AI-assisted insights, propensity scoring, anomaly detection, and budget scenarios. Partners with marketing, sales, finance, data engineering, security, and legal to improve data quality, responsible AI usage, and investment decisions. Delivers executive reporting and mentors analysts.

An 18-month rotational investment management program for recent graduates, featuring three six-month rotations across multiple asset classes. Fundamental analysts conduct investment research, valuation, financial modeling, due diligence, portfolio monitoring, and prepare recommendations. Quantitative analysts build models, analytical tools, data infrastructure, backtests, risk analytics, portfolio construction analyses, and visualizations. Candidates collaborate with investment professionals, support portfolio management, and use financial and quantitative technologies. Relocation to Calgary is required, with potential extension or full-time employment after completion.