Top Machine Learning Jobs in Toronto

Analyze microbiome and longitudinal clinical data, develop biomarkers and microbiome metrics using statistical and machine learning methods, and create visualizations, reports, protocols, and study reports. The role supports research, product development, scientific publications, proposals, and clinical studies while collaborating independently with microbiome experts in a remote-first startup environment.

Build scalable backend platform capabilities, infrastructure, tooling, and ML frameworks for Walmart’s Membership Experience Engineering team. Lead the full software development lifecycle, apply cloud-native architecture, design APIs and resilient systems, integrate AI and machine learning components, conduct design reviews, and ensure deployment, observability, and maintenance. Mentor engineers, influence technical decisions, collaborate with stakeholders, and use data-driven practices to improve engineering productivity and product outcomes.

Lead productionization of deep learning models and scale ML systems for marketing personalization. Build, optimize, monitor, and deploy reliable, high-performance AI products; integrate research into product, mentor engineers, and drive adoption and evaluation (A/B testing, monitoring, orchestration).

Lead development and productionization of fraud prediction models (tabular, graph, behavioral). Build scalable feature pipelines, run experiments, integrate models into real-time/batch systems, monitor model and data health, and collaborate cross-functionally to maintain model reliability and performance.

The Senior Machine Learning Engineer will design, implement, and optimize MLOps systems, mentor teams, and solve complex problems while supporting scalable ML models and strategies.

Manage and mentor a growing team of engineers, prioritize projects, ensure high-quality deliverables, and foster an inclusive work environment.

The Head of Education & Community at Siena is responsible for developing product education, onboarding programs, community events, and market education resources. This role involves creating curricula, conducting workshops, and supporting sales with training and demos while leveraging AI for teaching efficiency.

As a Senior Data Scientist, you will design, build, and operate machine learning and GenAI systems, influence technical direction, and mentor junior data scientists, focusing on production ML systems.

As a Machine Learning Engineer II, you will build advanced ML models and systems, collaborate with teams, and improve personalized experiences on Pinterest.

Design and deploy large-scale recommender and personalization systems using modern deep learning and ML techniques. Build end-to-end data pipelines and collaborate across product teams to experiment, evaluate, and improve retrieval and ranking models for shopping and other surfaces.

Lead complex programs in Ads, focusing on AI-assisted campaign experiences. Collaborate across teams to translate strategies into execution and improve advertiser performance.

As a Lead Applied Scientist, you will define research strategy, mentor teams, and innovate AI/ML solutions for legal and other professional sectors.

The Senior Research Engineer will develop and deliver scalable ML solutions, collaborate with teams, and apply innovative technologies in AI to impact professionals in legal and accounting fields.

Design and build platforms and tooling to support fraud investigations and operational workflows. Work with large datasets and LLM/AI systems in production, collaborate with data scientists to automate manual reviews, improve system reliability, and increase development velocity. Hybrid role based in Toronto (3+ days/week).

Embed within the M&A team to identify, prioritize, prototype, and deploy AI and data-science solutions that speed deal sourcing, diligence, pricing, and integration. Architect the data foundation, translate between business and technical teams, build ROI-backed use cases, ship prototypes into production, drive adoption, and measure model performance, time saved, decision quality, and dollar impact across the transaction lifecycle.

Lead Security Architect designs and implements enterprise security frameworks and roadmaps, secures cloud (Azure/AWS), applications, CI/CD, IAM and AI systems, performs risk assessments and threat modeling, evaluates security tools, conducts financial analysis for security technologies, manages deployments and stakeholder coordination, mentors teams, and enforces alignment with global security standards.

Develop pricing and quantitative risk models for assigned portfolios, conduct quantitative research and stress testing, document data flows and systems, monitor portfolio risk, provide analytical and technical support, and recommend risk mitigation in line with regulatory and enterprise Risk Management Frameworks.

Design and lead architecture for real-time screening, detection, and investigation systems to enforce sanctions and AML rules. Build high-throughput, low-latency APIs and distributed pipelines, enable multi-tenant risk-as-a-service, integrate ML/LLM tooling for reviewer augmentation, drive cross-functional alignment, mentor engineers, and ensure operational reliability including on-call ownership.

Lead planning, execution, and optimization of cross-functional hardware and software projects for robot development, manufacturing, testing, upgrades, and deployments. Coordinate mechanical, electrical, embedded, systems, manufacturing, and operations teams, define scope and timelines, mitigate risks, implement process improvements, run sprints, and maintain project documentation and stakeholder communication.

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.

Leads Android software development across design, coding, testing, deployment, debugging, and production support. Develops scalable and secure mobile solutions using Kotlin and modern architectures, integrates AI and ML capabilities, conducts code reviews, supports CI/CD and DevOps activities, monitors performance, and promotes engineering best practices. Collaborates with cross-functional teams, owns feature delivery, mentors peers, and contributes to Walmart+ customer-focused platform improvements.

Develops, calibrates, validates, and maintains credit risk models, including PD, LGD, and EAD models, for lending, collections, loss forecasting, capital planning, and stress testing. Analyzes large datasets, evaluates model performance, documents model changes, supports systems testing, and communicates statistical insights to stakeholders and management. Collaborates across the credit union to improve risk-return strategies and reporting.

Lead design and implementation of high-throughput, low-latency programmatic bidding systems. Collaborate with ML engineers to deploy real-time inference, optimize distributed microservices, run experiments and quantitative analysis, and partner with product to ship scalable features. Foster operational excellence, mentor engineers, and continuously improve performance and reliability.

Lead high-complexity data science initiatives improving search ranking, retrieval, relevance, and evaluation. Develop and deploy production machine-learning and NLP solutions, investigate LLM-based ranking features, optimize SQL queries, and work with sparse privacy-preserving data. Define strategies for ambiguous problems, guide other data scientists, collaborate with product teams, and own projects from scoping through production.

Analyze and optimize payment-risk rules, thresholds, customer segments, and manual-review strategies. Use SQL, Python, experimentation, and decision-science techniques to reduce false positives, customer friction, operational workload, and payment losses. Investigate fraud patterns, build monitoring and reporting, evaluate risk changes, and provide practical recommendations while partnering with Risk Operations, Product, Engineering, and Data teams.