Top Machine Learning Jobs in Toronto

The Machine Learning Engineer will develop and deploy ML models, leveraging analytical skills to automate processes and reveal data value.

The role involves end-to-end machine learning lifecycle management, including data processing, model development, rigorous testing, and production deployment, focusing on large quantitative models for AI-first products.

The Machine Learning Engineer will manage the ML lifecycle, develop models, ensure performance, and collaborate with a diverse team to build AI-first products.

Responsible for software integration and quality for Cerebras AI platform, focusing on automation, debugging, and cross-team collaboration. Develops QA strategies and testing methodologies to ensure product quality.

Build and deploy generative ML features for Ideogram's app and API: fine-tune and ship models, design evaluations and datasets, run error analyses, monitor reliability, and collaborate with research, infra, product, and clients to meet quality, latency, and cost goals.

Build scalable ML data pipelines for Waabi's autonomous driving platform. Design, optimize, and manage datasets and training processes while collaborating with scientists and engineers.

The Principal Platform Engineer will design and build scalable ML platforms, develop cloud-native infrastructure, and manage the model lifecycle in enterprise environments, focusing on MLOps capabilities and mentoring engineers.

RAVL is seeking Senior ML Platform Engineers to build and scale machine learning platforms, develop ML pipelines, and support MLOps workflows in cloud environments.

Develop and deploy scalable deep learning and reinforcement learning systems, optimize performance, and collaborate with data teams at Quincus.

Lead the development of device intelligence and fingerprinting systems by designing backend services, collaborating on integrations, and applying advanced machine learning algorithms to enhance security and combat fraud.

Lead development of back-end systems and end-to-end data/ML pipelines, build and deploy generative AI workflows, design and consume performant APIs, contribute to AI/ML architecture, mentor engineers, and communicate progress to leadership.

Develop and maintain backend services and data pipelines to support AI/ML and GenAI workflows. Build and consume secure, scalable APIs, work with microservices on Kubernetes, collaborate on design and code review, and grow applied ML skills under senior mentorship.

As a Machine Learning Engineer, you'll develop scalable ML solutions using Google Cloud Platform and Vertex AI, collaborate with teams, and operationalize models from ingestion to monitoring.

Build core, shared ML capabilities for a production platform: foundation models for documents, extraction agents, model routing, unified evaluation, and continuous improvement pipelines. Own end-to-end capability development, production deployment, and cross-team support to improve accuracy, latency, cost, and reliability across institutional workflows.

As an ML Engineer at Luma AI, you will design high-performance model serving pipelines, manage GPU resources, and optimize CI/CD for large-scale AI systems.

Build and operate secure, scalable ML pipelines and hybrid GPU infrastructure for training, fine-tuning, serving, and governing open-source LLMs and agentic apps. Implement CI/CD, model versioning, observability, benchmarking, security controls, and integration with RAG/agent tools and vector DBs to support production deployments across tenants.

Design, train, evaluate, and optimize agent-native LLMs and RAG pipelines for enterprise use. Build training and RL pipelines (RLHF/DPO/PPO), embedding-based memory, evaluation harnesses, observability, and inference optimization across cloud and on-prem environments.

Design and build machine-learning-driven robotic systems that operate in the real world, support portfolio company growth, influence product and technical direction, and advance company and career outcomes.

Senior Machine Learning Engineers will develop ML products and lead projects end-to-end—from initial concept through final launch—across S32 portfolio companies. Candidates should drive product direction, have strong ML engineering and productization experience, and make significant business impact.

As a Senior Machine Learning Engineer, you will design, develop, and deploy ML systems, mentor team members, and collaborate on advanced AI applications in tax research.

Work with the product team on automating data ETL pipelines, deploying ML models, and connecting ML models to user interfaces. Collaborate with cross-functional teams on product development.

As a Sales Executive, you will hunt for new clients, manage enterprise accounts, and create revenue streams while collaborating with internal teams.