AI Jobs in Toronto

View detail

Translate business requirements into requirements for AI/ML models; prepare data to train and evaluate AI/ML/DL models; build AI/ML/DL models by applying state-of-the-art algorithms, especially transformers; leverage existing algorithms from academic or industrial research when applicable; test, evaluate, and benchmark the AI/ML/DL models, and publish the models, data sets, and evaluations; deploy models in production by containerizing the models; work with customers and internal employees to refine the quality of the models; establish continuous learning pipelines for models with online learning or transfer learning; build and deploy containerized applications on cloud or on-premise environments.

View detail

Contributors design original computational statistics problems that simulate real mathematical research workflows, creating problems requiring Python programming to solve using libraries like Numpy, SciPy, and Sympy. They ensure problems are computationally intensive and require non-trivial reasoning chains in areas such as number theory, combinatorics, graph theory, and numerical analysis. The problems are based on real research challenges or practical mathematical applications. Contributors verify solutions using Python with standard mathematical libraries and document problem statements clearly with verified correct answers.

View detail

Contributors may generate prompts that challenge AI, evaluate AI-generated solutions for correctness, assumptions, and logic, improve AI reasoning to align with first principles and accepted standards, and apply structured scoring criteria to assess multi-step problem solving.

View detail

Contributors may generate prompts that challenge AI; evaluate AI-generated solutions for correctness, assumptions, and logic; improve AI reasoning to align with first principles and accepted standards; and apply structured scoring criteria to assess multi-step problem solving.

View detail

Design original computational statistics problems that simulate real mathematical research workflows; create problems requiring Python programming to solve using libraries such as Numpy, SciPy, and Sympy; ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes; develop problems requiring non-trivial reasoning chains in areas like number theory, combinatorics, graph theory, and numerical analysis; base problems on real research challenges or practical applications from mathematical practice; verify solutions using Python with standard mathematical libraries; document problem statements clearly and provide verified correct answers.

View detail

Ship critical infrastructure by managing real-world logistics and financial data for the largest enterprise in the world. Own the why by building deep context through customer calls and understanding Loop’s value to customers, pushing back on requirements if a better, faster solution exists. Work across system boundaries with full-stack proficiency, including frontend UX, LLM agents, database schema, and event infrastructures. Leverage AI tools to automate boilerplate work, focusing on quality, architecture, and product taste. Constantly optimize development loops, refactor legacy patterns, automate workflows, and fix broken processes to raise the velocity bar.

View detail

The Staff Software Engineer is responsible for owning the end-to-end technical vision for the EDR telemetry and detection workstream, rallying the team from concept through shipping, iterating, and deprecating. This includes producing production code contributions in a modern backend language such as Go, Rust, or Python within a service-oriented environment and setting technical standards through design reviews, code quality, and operational discipline by example. The role involves mentoring engineers, building frameworks and architecture to enable high performance, partnering with the hiring team on recruiting and leveling engineers, and holding the team accountable for outcomes by managing risks and tradeoffs early and in writing. The engineer translates ambiguous product goals into concrete technical roadmaps, makes decisions regarding build versus buy or integration with business context, partners closely with product management in PRD reviews and sprint planning, and sequences MVP development effectively. Domain expertise is required in EDR platforms including telemetry, API level, detection logic, alert triage, and SOC team workflows. The engineer builds ground truth datasets, manages false positive and false negative tradeoffs and confidence scoring, and owns the detection and measurement methodology, including ground truth methodology, confidence scoring, calibration, and defining what constitutes correct tuning recommendations. The position requires collaboration, and contributing both to leadership and hands-on coding, and may include up to 10% travel.

View detail

Design and build abstractions and platform-level systems that improve all of Harvey’s agentic products; own infrastructure for model integration, routing, and evaluation that helps Harvey choose and deploy the right foundation model for any given context; build evaluation frameworks and tooling that let every team across Harvey iterate on AI quality effectively; partner closely with product engineering teams, PMs, and design to launch cutting-edge AI products; evaluate, prototype, and integrate the latest advancements in AI and agentic systems as they emerge.

View detail

Responsibilities include translating business requirements into requirements for AI/ML models, preparing data to train and evaluate AI/ML/DL models, building AI/ML/DL models using state-of-the-art algorithms especially transformers, testing and evaluating models, benchmarking quality, publishing models and datasets, deploying models in production by containerizing them, working with customers and internal employees to refine model quality, establishing continuous learning pipelines with online or transfer learning, and building and deploying containerized applications on cloud or on-premise environments.

View detail

Define and drive architecture for scalable, extensible Reinforcement Learning Environments (RLE) systems and data pipelines. Lead development of platform capabilities enabling rapid domain creation. Partner with Research, Product, and Operations to shape strategy and execution. Set standards for reliability, observability, performance, and data quality. Mentor engineers and elevate engineering excellence across the team. Identify and solve systemic bottlenecks in scaling environments and data generation.