Top Machine Learning Jobs in Toronto

Owns production machine learning systems and automated decisioning, including graph-based models, RAG systems, real-time serving, monitoring, evaluation, drift detection, and retraining. The role establishes ML engineering and MLOps standards, shapes platform strategy, mentors scientists, and partners with senior leaders. Responsibilities include developing SignalGraph, improving LLM evaluation, applying modern deep learning methods, and communicating technical decisions and uncertainty to cross-functional stakeholders.

Design, engineer, and optimize protein-based biosensors that convert neuronal activity into imaging signals. Build and characterize protein variants using computational design, directed evolution, combinatorial libraries, mammalian expression systems, and high-throughput assays. Collaborate with machine learning, biochemical, structural biology, and computational teams to model sequence-function relationships, develop screening methods, understand biomolecular mechanisms, and advance constructs toward in vivo applications.

Leads product strategy, roadmap, and execution for threat intelligence, threat hunting, and detection capabilities. Partners with engineering, architecture, security research, executive, sales, and SOC teams to drive innovation, product launches, OKRs, portfolio prioritization, and customer security outcomes. Requires deep cybersecurity expertise and extensive senior product management experience, with strong knowledge of emerging threats, AI applications, and enterprise security products.

Own new business and account growth across hyperscalers, neoclouds, enterprises, and frontier AI labs in the Americas. Manage complex technical sales cycles, layered OEM/ODM and channel routes, capacity commitments, design-ins, pipeline forecasting, contracts, and executive relationships. Translate semiconductor performance, power, and TCO advantages into commercial outcomes while partnering with product, engineering, marketing, legal, and finance. Build a qualified pipeline through an existing customer network and represent the company at industry events.

As a Staff ML Engineer, you'll design and operate Samsara's ML platform, partnering with teams to deliver scalable ML solutions that enhance safety and efficiency in physical operations.

Lead the technical direction and hands-on development of ML and operations research systems powering delivery dispatch. Build pricing, quote selection, forecasting, routing, batching, fleet recommendation, LLM, optimization, evaluation, and MLOps capabilities. Translate operational constraints into reliable models and solver formulations, establish production ML standards, and mentor engineers. The role partners with Product and leadership to deliver measurable improvements in revenue, margin, utilization, conversion, and delivery reliability.

Lead Tilt’s product design function across consumer financial products. Set design vision and quality standards, manage and grow a team of six designers and one researcher, build research capabilities, influence product strategy, scale the design system, advocate for users, and partner with Product, Engineering, Marketing, and executive leadership. Drive AI-enabled design practices and ensure product experiences deliver measurable user and business impact.

Lead a data science team to design and deliver merchant registry and profiling ML capabilities for onboarding and monitoring. Define strategy, guide model development and validation, promote reusable data assets, partner with Product and Engineering, drive Agile delivery, hire and mentor team members, and communicate impact to senior leadership to scale production ML systems on modern cloud/data platforms.

Build and maintain cloud infrastructure, APIs, data and inference pipelines, databases, CI/CD systems, monitoring, and Kubernetes deployments for machine learning products. Develop Python backend applications, integrate model registries and MLOps frameworks, monitor model drift and vulnerabilities, optimize Google Cloud infrastructure, and support testing, documentation, code reviews, and AI platform roadmap planning.

Lead research in computer vision, spatial perception, and human-centered AI. Develop real-time 2D/3D vision, tracking, gesture recognition, compact model, and agentic AI systems. Integrate foundation models with efficient perception pipelines, optimize for hardware constraints, establish datasets and evaluation metrics, build prototypes, support product integration, and mentor researchers and engineers.

Design and build production agentic AI systems for enterprise clients. Develop autonomous agents using Glean, OpenAI/GPT Enterprise, and AWS or GCP. Own agent lifecycles from architecture and implementation through deployment, monitoring, evaluation, and iteration. Apply tool calling, RAG, multi-step orchestration, memory, prompt engineering, structured outputs, permission-aware retrieval, and observability to create reliable, secure, maintainable enterprise-scale AI solutions.

Leads Salesforce Controllership’s finance systems strategy, ERP ownership, record-to-report transformation, and AI-driven accounting automation. Oversees Workday ERP architecture, data integrity, enhancements, production issue resolution, process redesign, and cross-functional alignment across Accounting, Finance, IT, Engineering, Tax, Treasury, Legal, and M&A. The role drives global controllership initiatives, strategic decision support, executive communications, and transformation programs that improve efficiency, accuracy, and scalability.

Lead development of scalable molecular dynamics pipelines, integrating physics-based models with machine learning frameworks to enhance molecular engineering and experimentations.

Leads total fund data science, AI-enabled analytics, semantic modeling, governed data products, and investment reporting capabilities. Designs analytical models, ETL/ELT pipelines, semantic metric layers, data marts, and LLM/RAG interfaces across enterprise platforms. Provides technical oversight, governance, quality controls, stakeholder influence, and strategic delivery across Finance, Investments, Technology, and Data. Coaches colleagues and advances responsible AI adoption, reporting automation, investment insight generation, and operational efficiency.

Lead the underwriting and credit risk validation sector within Model Risk Management. Manage and mentor a quantitative team, perform hands-on validations of complex ML models, assess data integrity and model performance, identify and remediate weaknesses, and partner with stakeholders to enhance the enterprise MRM framework and meet audit and regulatory requirements.

Own Sophos EDR strategy, roadmap, technical product development, AI-powered experiences, and go-to-market execution. Translate customer needs, threat research, telemetry, competitive intelligence, and business goals into product priorities. Partner with engineering, security operations, threat research, marketing, sales, and customer success to deliver endpoint detection, investigation, threat hunting, response, reporting, and XDR/MDR upgrade capabilities while driving adoption, revenue, and customer outcomes.

Serve as the technical sales and customer-facing expert for an AI platform. Responsibilities include conducting product demonstrations, supporting proofs of concept, advising on architecture and development, planning professional services projects, tuning retrieval and agentic pipelines, responding to RFPs, supporting customer adoption, and communicating product feedback internally. The role requires strong AI, cloud, on-premise, application development, storytelling, and enterprise customer engagement experience.

Lead the Rainmaker Ad Server engineering domain, setting technical direction and roadmap for high-scale, low-latency advertising systems. Oversee architecture, reliability, observability, incident management, modernization, and cross-functional delivery with Product, Data Science, ML, and Ads Operations. Build and develop engineering managers and senior engineers while improving execution, scalability, monetization efficiency, and operational excellence across business-critical distributed systems.

Build and operate a reliable ML platform for large-scale distributed GPU training. Responsibilities include Kubernetes infrastructure, developer-facing CLIs and SDKs, AWS infrastructure, data and artifact management, experiment tracking, model registries, CI/CD, observability, documentation, security guardrails, and platform adoption. The role partners closely with researchers and engineers to improve training speed, reliability, usability, and cost efficiency for autonomous transportation systems.

Develop and deploy production machine learning solutions for retail personalization and loyalty programs. Responsibilities include recommender systems, ranking models, offline evaluation, large-scale PySpark feature engineering, ML platform design, cloud training pipelines, agentic AI solutions, workflow orchestration, and production reliability. The role also involves technical leadership through code reviews, documentation, mentorship, and communication of technical and business concepts.

Develop and deploy production machine learning solutions for retail personalization and loyalty offers. Build recommender and ranking models, large-scale PySpark feature pipelines, offline evaluation frameworks, ML platform infrastructure, and agentic AI systems. Operate cloud training workflows and analytical pipelines while improving reliability, scalability, and engineering standards through mentorship and code review.

Own the demand planning product roadmap, translating customer and market needs into forecasting, demand sensing, scenario planning, and AI-enabled capabilities. Lead discovery, prioritization, and delivery with Engineering, Data Science, UX, and go-to-market teams. Define success metrics, monitor performance, represent customer needs, and drive cross-functional alignment. The role requires deep demand planning expertise, enterprise software product management experience, strong analytical and communication skills, and up to 20% travel.

Own and grow a territory of SMB accounts, build pipeline through outbound and inbound prospecting, close new business, and expand existing customer relationships. The role requires quota ownership, solution selling, negotiation, forecasting, presentations, and explaining cybersecurity and IAM concepts to customers. The Account Executive collaborates with BDRs, solution teams, channels, onboarding, and customer success while using structured qualification and sales processes to achieve growth targets.

Develop and deploy algorithmic and machine learning solutions for Lyft’s mapping products, improving route recommendations, traffic prediction, and travel-time estimates. Responsibilities include data exploration, feature engineering, experimentation, production ML systems, real-time inference, batch pipelines, monitoring, optimization, and metric development. The role partners with Engineering, Product, Operations, and Science teams to translate complex mobility and geospatial challenges into scalable solutions.

Own the full product lifecycle for AI-native insurance agencies and loss prevention services, spanning customer experiences, internal tools, automation, and integrations. Rapidly prototype MVPs with AI-assisted tools, define success metrics, analyze quantitative and qualitative feedback, align cross-functional stakeholders, and ensure regulatory and data security compliance. The role reports to the CEO and requires autonomous product ownership in a fast-moving startup environment.