Top Machine Learning Jobs in Calgary

Build infrastructure and tooling for AI evaluation, including distributed execution, task-set validation, graders, human-review calibration, statistical reporting, online feedback loops, and offline-to-online analysis. Partner with product, engineering, data, and ML teams to create reliable evaluation workflows that support faster, statistically sound AI product decisions.

Build infrastructure and tooling for AI evaluations, including distributed scoring engines, task-set validation, grader systems, human-review calibration, statistical leaderboards, online feedback capture, and offline-to-online evaluation loops. Partner with product, engineering, data, and ML teams to deliver reliable, observable evaluation workflows that support faster and more trustworthy AI product development.

Leads technical direction for GitLab’s Core DevOps services, covering CI, CD, planning, and source control. Defines architecture, quality metrics, migration strategies, and technical roadmaps; addresses systemic risk and complex design decisions; prototypes and writes code when needed; partners with Infrastructure, Security, SRE, and Product; adopts AI capabilities; mentors senior engineers; supports incident response and senior technical hiring.

Develop and optimize machine learning algorithms to improve advertising ROI and campaign performance. Build production code in collaboration with data engineers, prototype algorithms and data pipelines, test them against historical data, and iterate based on results. The role requires strong machine learning, statistics, optimization, coding, data structures, and algorithmic skills, along with the ability to break down ambiguous problems into actionable steps.

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

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.

Leads architecture and development of AI-native networking and security operations platforms. Builds LLM and agentic workflows, telemetry pipelines, anomaly detection, root-cause analysis, threat investigation, world models, RAG systems, and closed-loop remediation. Drives cloud-native production deployment across Kubernetes, microservices, and multi-cloud environments while partnering with product and engineering teams. Mentors technical teams and leads innovation from experimentation through scalable production systems.

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.

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 end-to-end product design for Clio’s fintech and billing experiences, from discovery and prototyping through launch and iteration. Partners with Product, Engineering, and customers to create accessible, high-quality workflows, improve design systems, and deliver trustworthy AI-powered features. The role requires strong interaction, information architecture, usability, visual design, communication, and mentoring skills, with a focus on measurable customer and business outcomes.

Own the vision, roadmap, execution, and quality measurement for Clio’s AI Search platform. Lead engineering and ML teams developing hybrid lexical and semantic search across documents, emails, and records. Improve relevance through ranking, query understanding, embeddings, A/B testing, click-through analysis, and offline evaluations. Coordinate across Clio products while balancing relevance, latency, cost, permissions, and customer AI preferences. Translate customer search needs into measurable product improvements and guide both new product development and platform scaling.

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.

Leads the Liquids Pipelines data modernization roadmap, delivering scalable data pipelines, curated datasets, analytics, dashboards, automation, and AI-enabled solutions. Translates business and operational needs into technical designs, data models, governance practices, and multiyear delivery plans. Focus areas include power cost optimization, operational performance, data quality, and executive reporting. Partners with stakeholders and platform teams while mentoring analysts and advisors and guiding solutions from concept through sustainment.

Leads business analysis for asset application enhancements, modernization, integrations, reporting, and analytics initiatives. Translates operational needs into business, technical, data, and reporting requirements; maps processes; facilitates stakeholder workshops; supports solution design, testing, implementation, governance, change management, and user adoption. The role partners with technical teams, business leaders, vendors, and project teams to improve asset performance using enterprise applications, data platforms, dashboards, and predictive insights.

Leads the technical direction of a machine learning team focused on personalization, relevance, ranking, search, and advertising systems. Designs and scales end-to-end ML architecture, including training, evaluation, deployment, and serving. Partners with product, data, and engineering stakeholders, drives system design tradeoffs, mentors engineers, and ensures reliable production operation at consumer scale. The role also encourages using AI and LLM tools to accelerate development and explore new ML-powered features.

Evaluate AI model outputs on data science, statistics, machine learning, experimentation, and quantitative problems. Develop expert prompts, datasets, and reference solutions; identify statistical and methodological errors; and provide structured feedback to improve model performance. The role requires Python, machine learning, statistics, data analysis, experimentation, Pandas, and data modeling skills.

Implement customer projects using Python, machine learning, and deep-learning tools for engineering, physics, CAD, CAE, and manufacturing workflows. Develop proofs of concept, analyze project data, train customer teams, and translate customer feedback into product improvements. The role requires strong engineering and machine-learning knowledge, simulation experience, customer-facing communication, and travel to US customer locations.

Analyze financial and business data across ARR, retention, billing, subscriptions, and customer cohorts. Build interactive Hex dashboards, translate stakeholder questions into reporting, investigate financial variances, and automate recurring workflows using AI. Partner with Analytics Engineering to improve data models, reporting sources, data quality, and metric definitions. Present insights and collaborate with Finance, Sales, Marketing, RevOps, and Data and Analytics teams.

Lead the evaluation, training, fine-tuning, and production integration of machine learning and large language models. Build prompt engineering and model evaluation pipelines, optimize Elasticsearch-backed AI workflows, benchmark commercial and open-source models, and monitor production performance. Collaborate with product, engineering, and security teams to deliver safe, scalable AI capabilities while identifying improvements to data pipelines and engineering processes.

Lead the company’s AI product strategy, roadmap, and team. Oversee AI/ML optimization, generative AI, multi-agent systems, forecasting, attribution, experimentation, model governance, and deployment. Partner with engineering, data science, platform, GTM, and customer success leaders to scale reliable, explainable AI products, align executives, improve advertiser adoption, and drive measurable business outcomes.

Own full-cycle B2B sales for Fastino’s AI and LLM products, including outbound prospecting, qualification, demos, proofs of concept, negotiations, and closing. Build pipeline and GTM systems, manage channels and partnerships, support customer onboarding, translate customer insights into product direction, and establish repeatable sales processes. Initially operate as an individual contributor alongside founders, then hire and lead the sales and customer success organization.

Monitor, investigate, and respond to cybersecurity threats across customer environments within Sophos’s 24/7 MDR service. Analyze logs, security events, endpoint and network data; conduct threat hunting; communicate findings to technical and executive audiences; support issue resolution; research indicators of compromise, exploits, vulnerabilities, and adversary techniques; and provide recommendations to reduce customer risk.

Own the Data and ML product roadmap, focusing on predictive performance infrastructure, creator-brand matching, and marketplace intelligence. Translate data and machine learning capabilities into measurable value, define product metrics, run experiments, prioritize initiatives, and balance brand and creator needs. Partner with engineering, data, design, and leadership to deliver products tied to retention, contribution margin, and other commercial outcomes.