AI Jobs in Toronto

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The Senior Full Stack Software Engineer is responsible for owning technical delivery end-to-end, shaping the architecture of ML-powered applications, and leading implementation across cloud services, APIs, and modern front-end frameworks with Claude Code, the Claude Agent SDK, and the Claude API integrated into design, building, and shipping processes. They act as the technical backbone of their project pod, balancing hands-on development with technical leadership, architectural decision-making, and client-facing collaboration. Responsibilities include reducing project risk through proactive ownership of epic-level design and execution, improving technical decision-making via research and evaluation of solutions including AI tooling, increasing client confidence by leading technical discovery sessions and acting as a technical SME, leading feature and epic-level implementation, leading architecture and solution design for moderately complex solutions, championing AI engineering best practices within the pod, providing mentorship and technical leadership including code review and hiring assistance, engaging with clients for technical discovery and scoping, and implementing higher-level testing and quality strategies for deployed solutions and LLM-powered features.

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As the Defense / Edge Tech Lead, you will own the technical direction for deploying Deepgram's speech-to-text (STT) and text-to-speech (TTS) models to edge and embedded environments. Your responsibilities include leading the technical strategy for edge deployment, defining the architecture for on-device, on-premises, and air-gapped inference across diverse hardware targets. You will optimize models for edge and embedded platforms through quantization, pruning, distillation, and runtime optimization to meet latency, memory, and power constraints. You will partner with hardware vendors like Qualcomm and Motorola for SDK integration, performance benchmarking, and joint go-to-market efforts. Supporting defense customer requirements through AWS NatSec partnerships by translating mission requirements into engineering deliverables is also part of your role. You will design and build edge runtime infrastructure such as model packaging, deployment pipelines, OTA update mechanisms, and telemetry for devices in low or no connectivity environments. Deployments must be hardened for security-sensitive environments with features like secure boot chains, encrypted model storage, tamper detection, and audit logging. You will benchmark and validate performance across hardware platforms, establishing test suites for latency, accuracy, power consumption, and resource utilization. Collaboration with Research and Engine teams to influence model architectures toward edge-friendly designs is expected. Furthermore, you provide technical leadership to cross-functional teams on defense and edge projects, set engineering standards, review designs, and mentor engineers on systems and optimization practices.

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Testing of AI based conversational products; Monitoring and improving quality assurance process ensuring any agreed-upon standards and procedures are followed; Providing a high level of data quality awareness across multiple teams; Evaluating and identifying where enhancements in accuracy of models are required; Detailed testing feedback preparation to help the team to improve AI models.

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Craft, optimize, evaluate, and benchmark prompts to enhance AI performance. Design and refine client-specific prompts ensuring accuracy and relevance. Define tool descriptions for agentic frameworks to improve AI interactions. Improve prompts for clarity and performance, automate testing with scripts, and evaluate large language models (LLMs) to identify best-fit solutions. Develop evaluation frameworks and benchmark prompts to establish best practices. Collaborate with Customer Success and Data Science teams while maintaining clear documentation on prompt development and optimization. Stay current with advancements in natural language processing (NLP), experiment with new prompting strategies, and refine model-specific adaptations.

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Contributors design original computational chemistry problems that simulate real chemistry research workflows and create problems requiring Python programming to solve using libraries such as numpy, scipy, and chemical libraries. They ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days or weeks), develop problems requiring non-trivial reasoning chains in physical chemistry, quantum chemistry, and molecular modeling, base problems on real research challenges or practical applications from chemistry practice, verify solutions using Python with standard computational chemistry approaches, and document problem statements clearly while providing verified correct answers.

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Contributors may design original computational mathematics problems that simulate real mathematical research workflows, create problems requiring Python programming to solve (using Numpy, SciPy, Sympy), ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks), 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, and document problem statements clearly while providing verified correct answers.

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Design original computational mathematics 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.

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Design original computational mathematics problems simulating 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; verify solutions using Python with standard mathematical libraries; document problem statements clearly and provide verified correct answers.

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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 including transformers and leveraging existing algorithms from academic or industrial research, test and evaluate AI/ML/DL models, benchmark their quality, and publish the models, data sets, and evaluations, deploy models in production by containerizing them, work with customers and internal employees to refine model quality, establish continuous learning pipelines for models using online learning or transfer learning, and build and deploy containerized applications on cloud or on-premise environments.

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The Senior Data Intelligence Engineer is responsible for building and maintaining high-fidelity dbt and SQL models that serve as the foundational data for complex, usage-based revenue models. They develop tools and permissions frameworks enabling 'Analyst Agents' to query data sources such as Athena, correlate Salesforce churn signals, and identify API latency issues. The engineer acts as the technical liaison with the Engineering/Infrastructure team to ensure data contracts are reliable and ready for autonomous agents. They partner with the Head of Data to ingest and transform thousands of hours of unstructured internal call audio into queryable insights for go-to-market teams using Deepgram’s own models. The role includes maintaining a culture focused on automating manual and repetitive SQL tasks through code and agent systems rather than legacy dashboards.