Top Machine Learning Jobs in Ottawa

Leads strategic client engagements and executive relationships for manufacturing and automotive analytics, data, AI, and digital transformation programs. Oversees solution design, proposals, commercial growth, delivery governance, roadmaps, data-platform modernization, and AI initiatives. Manages portfolios up to $10M–$15M annually, develops executive recommendations, mentors multidisciplinary teams, and builds reusable industry capabilities across smart manufacturing, connected operations, supply chain, quality, and industrial AI.

Supports pharma, biotech, and MedTech data teams through web-based information sourcing, content review, data analysis, and quality assurance. The role includes spreadsheet work, prompt engineering for large language models, basic coding, and projects expanding platform data. This is an eight-month rotational co-op placement across two brands, focused primarily on data quality and sourcing rather than data engineering.

Lead the development, optimization, and production deployment of advanced machine learning algorithms for advertising effectiveness and ROI. Build scalable ML models, write efficient code, collaborate with machine learning engineers, prototype and test algorithms and data pipelines, and drive iterative improvements using historical data and rigorous performance analysis.

Leads strategic value advisory engagements for enterprise customers, developing ROI models, financial business cases, industry benchmarks, and executive presentations. Partners with sales, product, finance, marketing, and customer success teams to communicate economic value, support deal structuring, and influence C-level decisions. Also develops scalable value methodologies and mentors junior team members and sales staff.

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.

Build production agentic AI capabilities focused on monitoring, guardrails, evaluation, security, collaboration, and ROI. Perform machine learning tasks including exploratory data analysis, feature engineering, and model fine-tuning. Translate research into scalable software, rapidly prototype features, collaborate cross-functionally, and deliver production-ready code while exploring emerging agentic technologies and AI security techniques.

Lead a small team developing secure, reliable agentic AI capabilities. Responsibilities include building agent monitoring, guardrails, evaluation frameworks, and collaboration tools; performing machine learning tasks such as exploratory data analysis, feature engineering, and fine-tuning; translating research into production features; contributing code and technical designs; and mentoring engineers while setting technical direction.

Own the strategy, roadmap, and success metrics for AI security capabilities across the Pathfinder platform. Serve as the internal expert on agentic AI threats, identity security, and emerging attack techniques. Define requirements for securing AI agents, MCP servers, and orchestration frameworks; conduct market and competitive research; lead design partner programs; coordinate cross-portfolio product work; and support analyst, customer, marketing, and pre-sales engagements.

Leads Insurity’s enterprise cybersecurity, information security, privacy, HIPAA, AI governance, risk, compliance, and incident response programs. The CISO sets security strategy, advises executives and the Board, oversees cloud and application security, manages audits and third-party risk, directs major incidents, protects regulated data, and builds a scalable security organization. The role also embeds security into product development, DevOps, AI adoption, customer assurance, and business operations.

Leads the architecture, development, optimization, and production deployment of complex AI and machine learning systems. Builds scalable ML platforms, pipelines, and infrastructure; establishes engineering standards; guides responsible AI practices; and mentors engineers. Partners with product and leadership teams to shape AI strategy, communicates technical tradeoffs, and drives high-stakes initiatives focused on reliability, scalability, security, cost, and safety. The role requires deep generative AI, agentic systems, RAG, cloud, and production engineering expertise.

Leads global total rewards strategy and an eight-person team across compensation, equity, benefits, retirement, executive compensation, job architecture, pay equity, governance, and HR technology. Advises executives, boards, and Compensation Committees; oversees scalable programs, international expansion, audits, acquisitions, and public-company readiness. Partners with Finance, Legal, and external advisors while advancing analytics, automation, AI, and platforms such as Pave.

Lead end-to-end executive and leadership recruiting across business and technology functions, including talent mapping, sourcing, stakeholder partnership, hiring strategy, offer negotiation, and closure. Mentor recruiters, monitor pipelines and hiring metrics, and develop talent strategies for data science, analytics, data engineering, and machine learning roles. The position partners with senior business leaders and supports recruiting growth in a global analytics consulting environment.

Own the roadmap and delivery of Maestro, nesto’s AI mortgage platform. Translate complex lending workflows into PRDs, decision logic, and shipped products; collaborate with ML, engineering, underwriting operations, and business stakeholders; define performance metrics; build feedback loops between AI outputs and expert judgment; and guide product strategy in a regulated, high-ambiguity environment.

Leads global AI-first MarTech engineering strategy, architecture, governance, compliance, and delivery. Oversees scalable digital platforms, public-facing properties, GenAI applications, R&D, proof-of-concepts, and automated compliance capabilities. Builds and mentors a global team of engineering and AI professionals, manages vendors, and partners with marketing, business, cloud, security, privacy, infrastructure, and legal stakeholders to deliver secure, high-performance digital experiences.

Own the roadmap for Maestro, nesto’s AI mortgage platform. Translate complex lending workflows into PRDs, decision logic, metrics, and shipped products. Collaborate with machine learning, engineering, underwriting operations, and business stakeholders. Improve throughput, decision speed, accuracy, and operational capacity while creating feedback loops between AI outputs and expert judgment. The role requires strong structured thinking, communication, execution in ambiguity, and active use of AI tools throughout the product lifecycle.

Develop data-driven solutions to complex real-world problems using data science, machine learning, statistical modeling, and analytical reasoning. Candidates should currently work in a technical data science or machine learning role at a leading technology company, hold a quantitative undergraduate degree from a top-ranked university, and reside and work in an eligible English-speaking country.

Build Neuromorphic Labs’ enterprise go-to-market engine from the ground up. Identify strategic customers and design partners, generate pipeline, lead complex sales cycles, convert pilots into commercial deployments, and close early enterprise accounts. Shape positioning, messaging, pricing, customer narratives, and product priorities based on market feedback. Establish scalable GTM processes, tools, metrics, and operating rhythms while partnering closely with the founder and technical 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.

Design, develop, validate, manufacture, and deploy novel mechanical and electrical subsurface imaging instruments, including muon detectors and seismic systems. Integrate hardware-generated data with data systems and exploration workflows, manage system architecture and technical tradeoffs, coordinate internal teams and external vendors, and support remote field commissioning and deployment. The role also helps establish scalable hardware development processes while collaborating with data scientists, engineers, and geoscientists.

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.

Analyze the customer lifecycle across activation, engagement, retention, expansion, and renewal for a product-led SaaS business. Partner with Customer Success, Product, Growth, RevOps, and Data Engineering to develop analytical frameworks, identify growth opportunities, evaluate experiments, and provide actionable recommendations to senior leaders. Build data foundations and standards supporting digital success, AI, automation, forecasting, anomaly detection, and generative BI.

Conduct independent research on machine learning, knowledge representation, formal methods, AI agents, and trustworthy AI systems. Develop algorithms, models, prototypes, benchmarks, and rigorous experiments for enterprise-scale applications. Collaborate with scientists, engineers, and domain experts to translate operational constraints into deployable research systems. Evaluate reliability, robustness, privacy, interpretability, latency, cost, and process outcomes, while communicating findings through publications and internal or external presentations.

Build and support secure full-stack web applications, internal tools, APIs, data pipelines, and database schemas. Develop AI agents and LLM-powered solutions using Agentic AI, RAG, tool calling, guardrails, and Microsoft Foundry. Deploy machine learning models for predictive analytics, implement automated testing and CI/CD, resolve application and data issues, and modernize legacy systems. Collaborate with technical teams and business stakeholders to deliver scalable information and decision-support solutions.

This Staff Machine Learning Engineer role at Twilio is intended to support the company’s communications technology and AI initiatives. However, the posting is an incomplete template and does not provide specific responsibilities, qualifications, technologies, compensation, or detailed team information.

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.