Top Machine Learning Jobs in Toronto

The Director of Engineering will lead and scale Buzz Solutions' engineering organization, focusing on AI, infrastructure, and platform systems while ensuring best practices and high-quality solutions.

The Senior Account Executive will develop relationships with utility clients, lead sales initiatives, negotiate contracts, and maintain sales activities in Hubspot to achieve sales targets.

Lead the architecture, development, and implementation of an enterprise-grade agentic AI orchestration platform. Build reusable AI components and intelligent agents using Claude, LangGraph, RAG, vector databases, MCP, prompt engineering, and multi-agent workflows. Develop scalable FastAPI APIs, ensure AI security and responsible AI practices, support enterprise or regulated data environments, and provide technical leadership across architecture and engineering efforts.

Lead and own the ML/AI function: architect multi-modal agentic systems, define evaluation frameworks and datasets, productionize reliable scalable ML, ensure compliance, and build and mentor a high-performing ML team.

Bay Area-based hunter Enterprise Account Executive responsible for prospecting, developing, and closing new enterprise business for Anyscale. Manage complex sales cycles, achieve quotas, lead CxO engagements, qualify opportunities, collaborate with marketing, and drive ongoing account growth while advising customers on ML, compute, and cloud solutions.

Define and build Wave's internal risk and payments operations platform. Own strategy and roadmap for tools used by Risk, Fraud, Compliance, Support, Finance, and Payments Operations. Replace manual workflows with scalable decisioning, case management, and automation. Partner cross-functionally with Engineering, Data, and business teams to improve efficiency, controls, and customer outcomes.

The Performance Engineer - Inference will optimize model inference speed and throughput, debug low-level kernel performance, and develop tools to visualize performance data.

Design and implement system-level debugging, validation, and observability platforms. Build automated anomaly detection, visualization and analysis tools, and frameworks for failure classification and regression detection. Extend compilers, runtimes, and instrumentation for advanced profiling. Improve bring-up and low-level debug workflows, partner cross-functionally across hardware, firmware, compiler and runtime teams, lead high-impact initiatives, and support incident response and long-term corrective actions.

Lead research and design of large-scale Biological Foundation Models (BioFMs) using genomic and single-cell data. Develop novel architectures and pretraining paradigms, implement and train models at scale, collaborate with biologists and drug developers, mentor junior researchers, and publish findings in top-tier venues.

Design, prototype, and productionize machine learning models for real-world problems using structured and unstructured data. Implement scalable ML systems, apply statistical methods, rapidly prototype research papers, and collaborate with engineering teams to deliver data-driven solutions.

Design, prototype, and productize scalable machine learning solutions using LLMs. Collaborate with clients and teammates to build production-ready systems, rapidly iterate prototypes, apply statistics and ML, and implement tooling and infrastructure for deployment and model-driven features.

Lead the Compass agent product as the technical and science owner: design and ship agentic/LLM-powered features end-to-end, set evaluation and experiment frameworks, build data-driven models and tooling, and collaborate across backend, data, and frontend to raise agent quality and enable safe action-taking behaviors.

Own commercial strategy and operations for enterprise pharma and AI accounts: drive pilots-to-revenue, account expansion, pipeline management, pricing, forecasting, and deal strategy. Build cross-functional relationships, push deals through legal/procurement/IT, act as CEO counterpart to shape strategy and product roadmap, and be highly present in person with customers and at conferences.

Design and deploy closed-loop decision systems that estimate causal effects, quantify uncertainty, and optimize actions under constraints. Develop policy learning, bandits/decision-making, counterfactual evaluation, and production ML infrastructure to drive measurable business lift and safe automated decisions.

The Applied Scientist will transform raw data into actionable insights by developing machine learning models for personalization, ranking, and optimization to enhance conversion and revenue. This role emphasizes building production systems, frameworks for experimentation, and communication with team members regarding technical challenges.

Embed with clients to gather requirements and design AI-driven solutions for fraud and fincrime. Architect, train, evaluate, and deploy custom AI agents and workflows, optimize model performance, establish risk metrics and evaluation pipelines, and collaborate with engineering/product teams to scale into production.

Lead the product vision for innovative AI technologies, translating research into marketable products, and collaborating with cross-functional teams.

Seeking a Research Scientist to develop AI models for enhancing 3D reconstruction processes, requiring strong skills in computer vision and neural reconstructions.

The Junior Data Scientist will design and develop data models using Machine Learning and Analytics for trading and compliance in finance.

Lead the data and ML product vision: define roadmap, prototype models on noisy behavioral datasets, translate signals into user-facing features, partner with engineering to productionize ML, and generate insights to inform product roadmaps.

Lead and mentor an ML engineering team to design, build, and ship LLM/GenAI and agentic systems (retrieval, tool use, orchestration, RAG). Collaborate with MLOps, product, and data science, review code, evaluate models, preprocess complex unstructured data, and deliver scalable, reliable AI features for a SaaS product in an agile environment.

Define and execute the ML platform roadmap to accelerate data discovery, experimentation, deployment, inference, and AI integration. Partner with engineers and data scientists to remove workflow friction, build interoperable, scalable platform capabilities (inference, observability, foundation model support), analyze usage, and drive adoption through strong product experiences and cross-functional execution.

Develop scalable web applications using React and Next.js, implement backend solutions in Python, maintain DevOps practices, and collaborate with AI/ML teams to deploy applications.

The role involves designing and implementing machine learning systems for knowledge representation and temporal reasoning, focusing on organizational intelligence. Responsibilities include building LLM-powered pipelines, developing memory algorithms, and researching various ML techniques.

Lead enterprise AI strategy and roadmap; build and scale cross-functional AI/ML teams; architect and deliver LLM, RAG, predictive and data-heavy AI products; establish MLOps, governance, cloud-native platforms (Azure/Databricks/AWS/GCP); drive product lifecycle, KPI tracking, security, and responsible AI practices.