Top Machine Learning Jobs in Ottawa

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.

Supports responsible AI governance by developing frameworks, conducting risk and impact assessments, evaluating fairness and transparency, monitoring regulatory requirements, maintaining compliance documentation, educating stakeholders, and improving AI oversight processes. Collaborates with technical, legal, compliance, risk, and business teams to promote ethical, accountable, and trustworthy AI deployment.

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.

Translates business needs into AI solution requirements, assesses AI and automation opportunities, develops business cases, and supports solution design and delivery. The role facilitates stakeholder collaboration, testing, implementation, change management, reporting, benefits tracking, and continuous improvement across AI initiatives.

The AI Security Analyst assesses and mitigates risks across AI systems, machine learning platforms, and Generative AI applications. Responsibilities include threat modeling, vulnerability identification, security monitoring, incident response, root cause analysis, access control, policy development, compliance support, audit participation, and secure design reviews. The role partners with engineering, infrastructure, governance, and business teams to improve AI security capabilities and promote responsible AI adoption.

Leads operational oversight, performance analysis, governance, and continuous improvement for AI-powered products and platforms. Develops performance frameworks, executive dashboards, business insights, forecasts, and risk reporting. Partners with engineering, product, and operations teams to improve workflows, support automation, ensure compliance, and drive scalable AI operating models.

Tests and validates AI-powered products, machine learning models, and Generative AI applications. Designs test strategies, executes functional and regression testing, evaluates model outputs, identifies defects, analyzes quality metrics, documents results, verifies compliance, and collaborates with AI engineers and product managers to improve testing practices and release readiness.

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, LTV, and next-best-action decisioning. Design reliable signal contracts covering freshness, provenance, confidence, eligibility, and calibration. Lead feature pipelines, model serving, experimentation, monitoring, and feedback loops while evaluating fairness, risk, compliance, trust, and long-term customer impact. Collaborate across product, growth, data, platform, modeling, risk, and compliance teams.

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, lifecycle intelligence, and next-best-action decisioning. Design reliable signal contracts with freshness, provenance, confidence, and calibration guarantees. Lead experimentation, monitoring, feedback loops, and impact evaluation focused on fairness, trust, risk, compliance, and long-term engagement. Partner across product, growth, data, platform, modeling, risk, and compliance teams.

Executes the full CRM marketing lifecycle, including campaign strategy, audience segmentation, personalization, QA, deployment, testing, measurement, and optimization across email, SMS, and push channels. The role manages lifecycle campaigns, uses analytics and automation to improve fan engagement and lifetime value, and collaborates with marketing, analytics, technology, product, agencies, and vendors. English and Spanish fluency, CRM platform experience, HTML editing, KPI analysis, and Photoshop proficiency are required.

Consultative Sales Engineer responsible for driving complex sales opportunities, designing cloud data security and data risk management solutions, delivering proof-of-value engagements and technical demonstrations, advising executive stakeholders, supporting partner channels, and coordinating cross-functional resources. The role requires cloud security, IAM, networking, databases, encryption, privacy, data governance, and strong technical communication skills.

Lead the architecture and implementation of scalable distributed systems powering AI features for millions of users. Build production infrastructure, data pipelines, and model-serving interfaces using Python, SQL, Spark or Dask, AWS, and Kubernetes. Partner with applied scientists to productionize ML and LLM features, own operational reliability and technical debt, respond to incidents, and mentor engineers while improving engineering practices across the AI Products team.

Provides strategic and hands-on biostatistical leadership across pharmaceutical discovery, translational, biomarker, and early research programs. Designs studies, analyzes complex biological datasets, develops advanced statistical methods and reproducible R workflows, and translates evidence into portfolio decisions. The role collaborates with quantitative and scientific functions, supports publications and external partnerships, mentors statisticians, and advances data-driven analytical practices.

Deploy and integrate Arondite’s software with defence customers across Canada and internationally. Responsibilities include writing secure, robust code; interfacing with autonomous and robotic systems; integrating unfamiliar platforms and data sources; testing in field environments; gathering user feedback; refining implementations; and sharing field insights with engineering teams. The role requires customer-facing communication, autonomy, adaptability, and frequent on-site work, with Ottawa as the intended base.

Teach an in-person undergraduate FinTech course covering emerging financial technologies, cryptocurrencies, blockchain, cybersecurity, encryption, machine learning, automation, crowdfunding, financial intermediation, regulatory technology, regulatory risk, and law enforcement. The instructor will prepare students for FinTech and RegTech careers through current professional knowledge and practical instruction.

Own clinical quality for member-facing AI products by partnering with product, engineering, and clinical teams. Design clinical AI evaluations, define quality thresholds and rubrics, assess accuracy, omissions, hallucinations, medication-safety risks, and translate clinical reasoning into prompts and decision logic. Monitor evolving evidence and standards of care, validate product launches, and communicate findings to technical and clinical audiences.

Develop new enterprise and upper mid-market consulting business in the Omaha/Lincoln market, manage ongoing client relationships, identify business and technology needs, create proposals and statements of work, close customized solutions, cultivate channel partnerships, maintain CRM records, and collaborate with marketing and technical teams. The role is remote, requires residence within 25 miles of Omaha, and includes occasional travel up to 10%.

Serve as a technical presales consultant for FICO’s enterprise decision intelligence solutions. Lead client discovery sessions, design tailored solutions, deliver product demonstrations, develop proofs of concept and proposals, and manage RFI/RFP responses. Create pricing scenarios and ROI analyses, support competitive sales strategies, represent FICO at workshops and conferences, and educate sales teams. The role requires collaboration across product teams and travel of 10–25%.

Updates machine learning training and test datasets, refines annotation guidelines, initiates model training, and evaluates model performance. The role annotates synthetic text and image data, conducts manual quality analysis, identifies error patterns, reports anomalies, and produces findings on utterance quality, LLM evaluation, ASR bugs, and customer pain points. It also applies basic NLP and linguistic knowledge to ensure data accuracy and consistency.

Serve as the technical SME for compliance automation, overseeing GRC control mapping, evidence collection, integrations, and continuous monitoring. Partner with engineering teams to embed compliance into workflows, support audits and framework rollouts, assign control ownership, reduce audit burden, and maintain audit readiness. Lead cross-functional initiatives and communicate compliance risks, control status, and progress to technical and leadership audiences. The role requires cybersecurity framework expertise, program management, cloud and API fluency, GRC platform experience, and familiarity with AI-enabled compliance workflows.

The Cloud Inference Engineer is responsible for building and maintaining a scalable LLM inference platform, focusing on distributed systems and machine learning technologies while collaborating across teams to optimize performance.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.