Top Machine Learning Jobs in Toronto

Lead development of ML components for medical image reconstruction: define training/evaluation pipelines and metrics, productionize models (inference performance, monitoring, safe fallbacks), collaborate on hybrid physics-ML algorithms, and build tooling for experimentation and rigorous verification.

Design, build, train, and deploy production ML models and scalable pipelines. Implement MLOps practices, observability, governance, and lineage. Optimize model performance and costs, collaborate with product, risk, and engineering teams to integrate ML features into production systems.

Support Data Science and Regulatory Affairs to define and execute regulatory strategies for AI/ML-enabled medical devices. Translate ML concepts into regulatory documentation, create DHF and submission materials (510(k), EU MDR, Health Canada), maintain dataset and model documentation, and manage version control and change management.

Build and productionize real-time ML systems for consumer AI: serve and optimize large-scale diffusion and LLM inference, improve model performance (quantization, distillation, pruning), implement personalization, ranking, and moderation, scale GPU infrastructure, run experiments and A/B tests, and own reliability, observability, and cost-performance tradeoffs.

As a Customer Engineer, you'll bridge technical problems and client needs in analytics and ML, designing data solutions and ensuring high-quality feature delivery.

The Associate Product Manager will work with analytics, ML, and product teams to develop features based on data insights, ensuring effective data management and customer outcomes.

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.

The AI/ML Engineering Manager leads ML engineering teams, manages client engagements, shapes architecture, ensures technical quality, and drives business growth through ML operations.

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 a high-performance robotics simulation platform and its compiler stack. Design compute infrastructure and data flow for physics simulation and model training, optimize GPU codegen and JIT compilation, improve differentiable programming support, and contribute to open-source ML compiler projects while collaborating with simulation and robotics engineers.

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.