Top Machine Learning Jobs in Ottawa

The Forward Deployed Research Scientist engages directly with AI labs to develop data strategies, fine-tune models, run experiments, and collaborate on publishing research, all in a fast-paced client-focused setting.

Enterprise sales role responsible for full sales cycle in Canada: prospecting, building executive relationships, selling Feedzai's AI-driven fraud and AML RiskOps platform, meeting quota, developing business cases, and aligning with GTM strategy to drive adoption and long-term customer relationships.

Design, build, and operate production ML decision systems to detect and prevent payment fraud, account takeover, scams, and other abuse. Integrate diverse signals into low-latency serving and batch scoring, own feature pipelines and model lifecycle, develop AI-assisted triage and feedback loops, and partner cross-functionally to balance fraud reduction with legitimate customer access.

Design, build, and operate production ML systems that generate trusted signals for ranking, retrieval, recommendations, propensity/churn/LTV, and next-best-action decisioning. Define signal/data contracts, own feature and candidate generation through serving, experimentation, monitoring, and feedback loops, and evaluate long-term business impact, trust, fairness, and compliance. Partner across product, data, modeling, risk, and compliance and apply AI/agents to accelerate engineering and operations.

Support FP&A forecasting, budgeting, and rolling forecasts; build driver-based financial models; analyze variances and deliver insights; create BI dashboards; validate data across ERP, Anaplan, and Snowflake; and automate reporting while enabling AI-driven forecasting.

Teach an online graduate/undergraduate systems optimization course (MIA5130). Develop and deliver lectures, assignments, and projects applying optimization and AI/ML techniques. Use Python/MATLAB and optimization solvers, supervise student work, evaluate performance, and integrate machine learning–enhanced and heuristic optimization methods for real-world applications.

Design, deploy, and maintain end-to-end ML risk solutions at scale to detect and prevent fraud, merchant risk, and credit loss. Partner with cross-functional teams, lead technical decisions, build ML tooling, monitor models in production, and investigate emerging abuse patterns to improve detection and decisioning.

Lead end-to-end personalization ML initiatives: build scalable ML pipelines, design CI/CD for models, monitor production performance, implement retraining and drift detection, partner with cross-functional teams, and mentor engineers.

Design, build, and implement automated testing and intelligent automation solutions. Develop scalable automation frameworks (Playwright), UI/API/performance/accessibility tests, integrate automation into CI/CD, mentor teams, define governance and KPIs, create SDDs, and collaborate with cross-functional stakeholders to improve quality and efficiency.

The Senior Product Manager will set product direction, own the development lifecycle, collaborate with teams, and ensure high-quality outputs. This role requires strong opinions on product quality and customer insights.

The Applied Scientist will drive research on efficient adaptive machine learning, deploy ML systems end-to-end, and contribute to product strategy, focusing on real-world impact and innovation.

The Fractional CTO will lead AI-focused projects, manage technical teams, and guide startups in technology development and strategic planning.

As a Staff Machine Learning Engineer, you'll lead AI initiatives using large-scale data, optimize ML models for edge devices, and collaborate with cross-functional teams.

Own the technical side of deal progression for airline deployments: qualify opportunities, run demos and POCs, author integration and deployment architectures (PSS, APIs, data feeds, DNS, identity, networking), respond to RFPs/security questionnaires, scope SOWs and NFRs, and build reusable demo/reference assets to drive adoption and expansion while liaising between sales, engineering, and customers.

Lead and execute product strategy for Everyday Money: define product thesis and roadmap, ship product lines, run AI innovation lab for agentic consumer experiences, manage and develop PMs, partner with design, Credit, and external partners, and own business economics and compliance for durable retention and monetization.

Lead and supervise stability studies and quality records to ensure cGMP compliance. Manage CAPAs, OOS/OOT investigations, stability data analysis and reporting, mentor laboratory staff, coordinate cross-functional stability projects, support audits, and drive continuous improvement and sustainability initiatives to optimize product shelf-life and regulatory readiness.

Drive independent, high-impact research on frontier AI topics, publish findings, collaborate widely, and mentor new researchers in the field.

Lead an interdisciplinary research program investigating mechanistic interactions in psychosis using neuroimaging, computational psychiatry, AI/ML, and multimodal data integration; supervise trainees, obtain external funding, teach, and translate discoveries into precision psychiatry and patient-centred care.

Lead architecture and technical strategy for AI-driven product quality systems using LLMs and agents. Build scalable evaluation frameworks, detect regressions, generate insights, and drive cross-functional adoption while mentoring engineers and defining standards for trustworthy AI.

Own and set technical direction for the Everand backend, building scalable, reliable systems for discoverability, personalization, and core consumption journeys. Drive architecture, platform health (CI/CD, staging, DBs, queues, cost, observability), mentor engineers, lead cross-team planning, modernize legacy systems, and partner with product, design, analytics, web/mobile, and ML to support experimentation and telemetry-driven decisions.

Build and maintain responsive Angular UI components, collaborate with UX and backend teams, implement REST APIs and Python backend scripts, integrate basic ML models into data workflows, and participate in the full SDLC within Agile teams while ensuring code quality and performance.

The Software Engineer, Perception develops autonomous aircraft systems to detect and manage wildfires using real-time data processing and machine learning techniques.

The Lead Metallurgist will oversee resource evaluation, audit geometallurgy models, liaise with teams, ensure quality control, and mentor junior staff.

Own a portfolio of large manufacturing accounts, drive net-new pipeline and full-cycle enterprise deals, build C-suite relationships, translate AI/vision capabilities into ROI, execute land-and-expand strategies, and travel 40-50% for on-site engagement and executive reviews.

Design, develop, and maintain high-performance, secure Java backend services and APIs. Ensure reliability, scalability, and cost-efficiency across microservices, databases, messaging systems, and capacity planning. Troubleshoot, profile, benchmark, and optimize systems (including ML applications) and collaborate with Kazakhstan stakeholders.