Top Machine Learning Jobs in Calgary

Leads product strategy, roadmap, and execution for threat intelligence, threat hunting, and detection capabilities. Partners with engineering, architecture, security research, executive, sales, and SOC teams to drive innovation, product launches, OKRs, portfolio prioritization, and customer security outcomes. Requires deep cybersecurity expertise and extensive senior product management experience, with strong knowledge of emerging threats, AI applications, and enterprise security products.

Own new business and account growth across hyperscalers, neoclouds, enterprises, and frontier AI labs in the Americas. Manage complex technical sales cycles, layered OEM/ODM and channel routes, capacity commitments, design-ins, pipeline forecasting, contracts, and executive relationships. Translate semiconductor performance, power, and TCO advantages into commercial outcomes while partnering with product, engineering, marketing, legal, and finance. Build a qualified pipeline through an existing customer network and represent the company at industry events.

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 the technical direction and hands-on development of ML and operations research systems powering delivery dispatch. Build pricing, quote selection, forecasting, routing, batching, fleet recommendation, LLM, optimization, evaluation, and MLOps capabilities. Translate operational constraints into reliable models and solver formulations, establish production ML standards, and mentor engineers. The role partners with Product and leadership to deliver measurable improvements in revenue, margin, utilization, conversion, and delivery reliability.

Lead Tilt’s product design function across consumer financial products. Set design vision and quality standards, manage and grow a team of six designers and one researcher, build research capabilities, influence product strategy, scale the design system, advocate for users, and partner with Product, Engineering, Marketing, and executive leadership. Drive AI-enabled design practices and ensure product experiences deliver measurable user and business impact.

Build and maintain cloud infrastructure, APIs, data and inference pipelines, databases, CI/CD systems, monitoring, and Kubernetes deployments for machine learning products. Develop Python backend applications, integrate model registries and MLOps frameworks, monitor model drift and vulnerabilities, optimize Google Cloud infrastructure, and support testing, documentation, code reviews, and AI platform roadmap planning.

Design and build production agentic AI systems for enterprise clients. Develop autonomous agents using Glean, OpenAI/GPT Enterprise, and AWS or GCP. Own agent lifecycles from architecture and implementation through deployment, monitoring, evaluation, and iteration. Apply tool calling, RAG, multi-step orchestration, memory, prompt engineering, structured outputs, permission-aware retrieval, and observability to create reliable, secure, maintainable enterprise-scale AI solutions.

Lead development of scalable molecular dynamics pipelines, integrating physics-based models with machine learning frameworks to enhance molecular engineering and experimentations.

Develop and implement safety framework methodologies for autonomous trucking readiness decisions. Build high-signal safety datasets across simulation and closed-course testing, establish feedback loops from road monitoring and safety events, validate safety evaluation pipelines, and create traceable safety artifacts. The role also requires cross-functional collaboration, technical mentorship, data analysis, statistical evaluation, and evidence-based safety case development for autonomous vehicles.

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.

Own Sophos EDR strategy, roadmap, technical product development, AI-powered experiences, and go-to-market execution. Translate customer needs, threat research, telemetry, competitive intelligence, and business goals into product priorities. Partner with engineering, security operations, threat research, marketing, sales, and customer success to deliver endpoint detection, investigation, threat hunting, response, reporting, and XDR/MDR upgrade capabilities while driving adoption, revenue, and customer outcomes.

Serve as the technical sales and customer-facing expert for an AI platform. Responsibilities include conducting product demonstrations, supporting proofs of concept, advising on architecture and development, planning professional services projects, tuning retrieval and agentic pipelines, responding to RFPs, supporting customer adoption, and communicating product feedback internally. The role requires strong AI, cloud, on-premise, application development, storytelling, and enterprise customer engagement experience.

Own the demand planning product roadmap, translating customer and market needs into forecasting, demand sensing, scenario planning, and AI-enabled capabilities. Lead discovery, prioritization, and delivery with Engineering, Data Science, UX, and go-to-market teams. Define success metrics, monitor performance, represent customer needs, and drive cross-functional alignment. The role requires deep demand planning expertise, enterprise software product management experience, strong analytical and communication skills, and up to 20% travel.

Lead applied AI and machine learning innovation for supply chain products. Develop prototypes and proof-of-concepts, evaluate emerging techniques, and guide promising ideas toward scalable enterprise capabilities. Provide technical leadership across research, product, and engineering teams while remaining hands-on with ML, generative AI, agents, LLMs, optimization, recommendation, and explainability. Influence architecture and technical direction, mentor colleagues, assess AI-generated code, and ensure solutions meet quality, security, maintainability, and customer-impact standards.

Own the full product lifecycle for AI-native insurance agencies and loss prevention services, spanning customer experiences, internal tools, automation, and integrations. Rapidly prototype MVPs with AI-assisted tools, define success metrics, analyze quantitative and qualitative feedback, align cross-functional stakeholders, and ensure regulatory and data security compliance. The role reports to the CEO and requires autonomous product ownership in a fast-moving startup environment.

Own strategic enterprise customer relationships, driving adoption, retention, measurable value, and expansion. Lead success reviews and executive engagements, translate supply chain challenges into solution strategies, build prototypes in Lyric Studio, train customer teams, coordinate forward-deployed engineers, partner with Sales on growth opportunities, and relay customer needs to Product and Engineering. The role requires deep supply chain expertise, technical fluency in modeling, planning, or AI/ML, and strong executive relationship skills.

Annotate and manage training/test datasets, run model training, evaluate model performance, perform manual QA and error analysis, and refine annotation guidelines for NLP/ML projects.

Conduct applied AI/ML research focused on time-series forecasting and generative AI for supply chain planning. Review literature, formulate hypotheses, design reproducible experiments, develop and evaluate models, analyze results, and communicate findings. Build research prototypes and collaborate with researchers, engineers, data scientists, and domain experts to assess product potential. Contribute to technical reports, demonstrations, publications, and patents under mentorship.

Monitors and improves the performance, reliability, and adoption of AI-powered systems. Analyzes operational metrics, develops dashboards and reports, identifies bottlenecks, supports incident resolution, documents procedures, and recommends process improvements. The role collaborates with AI engineers, product teams, business stakeholders, and operations leaders while supporting governance, compliance, risk mitigation, and continuous improvement initiatives.

Supports responsible AI governance by developing policies, maintaining control frameworks, conducting AI risk assessments, monitoring regulatory compliance, preparing reports, and advising legal, compliance, security, AI, and business stakeholders. The role also tracks governance metrics, supports audits, researches emerging regulations, and recommends improvements to AI governance programs.

Leads the strategy, roadmap, development, launch, and optimization of AI-powered products. Defines product requirements, priorities, success metrics, and business outcomes while coordinating engineering, AI, executives, and business stakeholders. Evaluates emerging AI technologies, supports responsible AI governance, manages feedback and adoption, and drives continuous product improvement.

Leads AI data annotation operations, managing workflows, resources, productivity, quality assurance, and project timelines. Coaches and develops annotation teams, supports recruitment, and collaborates with AI engineers, trainers, and product teams. Establishes annotation guidelines, conducts audits, resolves quality issues, and drives process improvements, automation, tool adoption, and operational excellence across AI data initiatives.

Develop and manage the strategy, roadmap, lifecycle, and delivery of AI-powered products. Translate business needs into product requirements, collaborate with engineering and AI teams, define success metrics, monitor adoption and ROI, and drive continuous improvements. The role also engages customers and stakeholders, evaluates emerging technologies, supports governance and compliance, and promotes responsible AI practices.

Own AI product backlogs, priorities, user stories, acceptance criteria, and delivery outcomes. Translate business needs into product requirements, collaborate with technical teams on solution design, facilitate stakeholder communication, support Agile delivery, and monitor product performance and adoption. The role also identifies AI-driven improvements, supports product releases, drives continuous improvement, and promotes responsible AI practices.

Leads business analysis for AI and automation initiatives by identifying adoption opportunities, developing business cases, gathering requirements, facilitating stakeholder workshops, supporting solution design and delivery, measuring benefits, and contributing to AI governance and continuous improvement.