Top Machine Learning Jobs in Ottawa

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.

Leads the ISAC System Verification organization’s test strategy, test management, quality objectives, coverage models, metrics, and risk-based validation. Drives AI-assisted testing, automation, continuous validation, and CI/CD-integrated verification for communications and sensing functionality. Partners with systemization, development, requirements, product, and release teams while communicating quality status and readiness to leadership. Coaches engineers, improves verification methodologies, and supports scalable quality practices for current ISAC products and future 6G solutions.

Develops AI-powered solutions for system verification, testing, and engineering workflows. Responsibilities include building Generative AI, LLM, RAG, and agentic AI applications; integrating AI tools with engineering systems; analyzing quality and testing data; supporting proof-of-concepts; and creating dashboards and visualizations. The role collaborates with verification engineers, test managers, and software developers to improve automation, productivity, and product quality.

Supports the governance, risk, and compliance program with a focus on third-party risk management. Conducts vendor assessments, maintains security policies and standards, performs risk analysis, supports SOC 2 audits, gathers evidence, tracks findings, and conducts internal audits. The role also uses AI and machine learning tools to improve GRC processes and promotes security awareness across the organization.

Develop, test, integrate, and maintain high-performance L1 embedded software for Ericsson 5G processing platforms and cloud-based RAN environments. Implement physical-layer baseband features across wireless standards and deployment scenarios, troubleshoot complex real-time systems, participate in code reviews and agile practices, maintain test coverage, and document designs and test plans. The role also involves exploring cloud-native optimization and applying AI/ML techniques where appropriate.

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 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.

Grade midterms, tests, and assignments for the Mathematical Machine Learning course. The role requires a student with at least 30 credits in mathematics and statistics and a strong understanding of the subject. Priority is given to graduate students in the Mathematics and Statistics Department. Work is performed in person during the Winter 2027 semester, with 50 total hours allocated entirely to grading.

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.

Develops backend software prototypes, evaluates architecture and technology solutions, researches emerging technologies, and tests prototypes for functionality, performance, and scalability. The role supports long-lead design and innovation initiatives within Kinaxis’s Architecture & Innovation team. Candidates should be computer science students or recent graduates with software development experience, cloud familiarity, and a willingness to experiment with modern technologies.

Supports in-orbit operations of GEO satellite subsystems, develops and maintains operating procedures, monitors ground RF infrastructure, and creates automated tools for data trending and performance analysis. The role uses Python, Linux, Git, databases, statistical methods, and machine learning concepts while contributing to satellite operations projects and occasional contingency support.

Lead the integrated robotaxi program plan from autonomy and vehicle readiness through safety validation, fleet operations, regulatory approvals, and commercial launch. Manage cross-functional dependencies, risks, milestones, KPIs, executive reporting, and scalable tracking processes while coordinating engineering, safety, commercial, operations, and external partners.

Own the Admin Workflows area of 1Password’s Enterprise Password Manager, defining a roadmap for administrator capabilities, dashboards, reporting, notifications, recovery, user confirmation, and MCP features. Partner cross-functionally with Engineering, Design, Marketing, and go-to-market teams to ship API-centric enterprise features, address customer problems, drive business outcomes, and measure product success. The role requires strong cybersecurity expertise, technical API knowledge, leadership, communication, and enterprise product management experience.

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.

Leads enterprise AI workforce transformation by redesigning operating models, business processes, employee roles, and human-agent collaboration. Designs and deploys AI agents, develops AI literacy and retraining programs, embeds adoption into performance and onboarding systems, and protects employee privacy. Directs readiness assessments, change programs, AI councils, champion networks, global training, stakeholder workshops, and adoption analytics to ensure sustained behavioral change across large organizations.

Leads product growth for 1Password’s AI opportunities by defining target markets, validating customer outcomes, running design-customer deployments, developing ecosystem partnerships, and scaling successful motions through sales, channels, and developer communities. The role owns strategy, customer adoption, integrations, commercialization, and cross-functional execution while working with enterprise customers, engineering, product, design, business development, and go-to-market teams.

Own and improve Quora’s second-phase ranking models for feed and digest experiences. Build end-to-end machine learning systems covering data pipelines, candidate extraction, feature engineering, model training, and production integration. Identify new product applications for machine learning, develop efficient algorithms with other ML engineers, and contribute methodologies for large-scale recommendation systems.

Build and deliver production AI systems across machine learning, generative AI, and agentic workflows. Design scalable ML and data pipelines, integrate heterogeneous data, evaluate emerging AI frameworks, and deploy secure cloud-native solutions. Collaborate with data science, product, security operations, and threat research teams to deliver customer value. Write and review code, troubleshoot production issues, measure AI quality, document technical decisions, and mentor engineers.

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.