Top Machine Learning Jobs in Toronto

Design, build, and operate ML data pipelines and unsupervised models to detect anomalous patterns in healthcare claims. Develop feature engineering, batch processing, and REST API model serving (FastAPI). Integrate with Oracle via SQLAlchemy/oracledb, maintain CI/CD and on-prem deployment workflows, collaborate with business teams to interpret clusters, and support model validation, operationalization, and audit artifacts.

The AI/ML Engineer will design and develop AI-powered applications, automate workflows, and integrate AI into BPM's daily operations, ensuring they deliver business value.

As a Machine Learning Engineer/Scientist, you will develop ML systems for cryopreservation research, manage data pipelines, and deploy models for scientific insight.

Build and operate high-throughput experimentation and recommendation infrastructure. Design APIs and services, use agentic AI workflows (LLMs) for development, ensure performance and reliability, participate in on-call, mentor teammates, and codify AI-native engineering practices.

Lead the development of the Wildfire Fuel Detection Model using ML techniques and geospatial data, collaborating with teams to ensure accuracy and robustness. Mentor engineers and drive architectural decisions.

Build and deploy NLP and generative AI solutions using transformer-based and other ML models. Develop LLM-based features, preprocess complex unstructured data, collaborate with engineering, product, and MLOps teams, and iterate in an agile, cross-functional environment to improve customer workflows and efficiencies.

Lead end-to-end development and deployment of AI/ML (including Agentic and LLM) solutions for enterprise-scale data reconciliation. Design models, manage pipelines and MLOps, analyze large structured/unstructured financial datasets, collaborate with engineering and business teams, maintain documentation, and deliver measurable impact across operations, risk, and finance.

Design and productionize ML systems for information extraction, retrieval, ranking and recommendation. Own full ML lifecycle from feature engineering to deployment, monitoring, and evaluation. Collaborate with data, infra, and product teams to build scalable, production-grade ML infrastructure and observability for both streaming and batch inference.

Design and implement system-level debugging, validation, and observability platforms. Build automated anomaly collection/analysis, visualization and root-cause tools, failure classification and monitoring frameworks. Extend compilers, runtimes and programming interfaces for profiling and instrumentation, improve bring-up and low-level debug workflows, lead cross-functional initiatives, support incident response, and establish debuggability and reliability best practices.

Implement and scale LLM training, fine-tuning, and post-training techniques (RL-based). Build evaluation and data pipelines, debug ML stack issues, optimize training/inference workflows, and ship maintainable ML infrastructure code.

Drive end-to-end ML model inference performance: build kernel- and system-level performance models, optimize kernel microcode and compiler algorithms, debug runtime performance on system and cluster, and develop tooling to visualize and analyze performance data from the Wafer Scale Engine and compute cluster.

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.

The Senior Machine Learning Engineer will develop and optimize AI image generation models, monitor production issues, and collaborate on ML solutions for generative features.

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.

Design, implement, and own a reusable on-device ML inference platform for resource-constrained devices. Integrate inference into RTOS firmware, develop drivers and DMA paths, debug on real hardware, train and optimize models for microcontroller-class hardware, and ship features to production while raising team expertise and using AI-assisted tooling.

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 a Senior Machine Learning Engineer, you will own the ML stack, develop generation systems, improve quality, collaborate cross-functionally, and build data pipelines.

Own and evolve ClickHouse's Python connectors and SDKs; build integrations with orchestration and transformation tools; design AI/LLM connector patterns for RAG, vector search, and feature pipelines; engage with open-source community; collaborate across product and engineering to prioritize and ship production-grade data integrations.

The Senior Software Engineer will develop AI/ML features, build robust APIs, create user-friendly UIs, and integrate AI models into ClickHouse Cloud.

Research and build efficient ML systems for large-scale LLMs and agentic RL: design algorithms and system techniques, prototype in training/inference stacks, run large-scale experiments, and translate findings into production or publications.

The role involves developing, adapting, and implementing machine learning algorithms for computer vision tasks in power grid analysis. Responsibilities include experimentation, error analysis, and project ownership.

The role involves designing scalable data pipelines for 3D, video, and sensor data, optimizing infrastructure, and productionizing ML models with researchers.

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.