AI Jobs in Canada

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Design computational material science problems to challenge a frontier AI model, ensuring each problem has an answer verifiable by code and requires a specialized tool such as ObsPy, instaseis, pyrocko, MITgcm, flopy/MODFLOW, or others. Pick an anchor tool and design problems based on its waveform-processing kernels, geophysical inversion routines, sub-surface flow solvers, or community-validated data pipelines. Write Python reference solutions, supply necessary input files and model or domain definitions, decide on numerical answers with domain-appropriate tolerance, and test the problems against the AI model through batches of parallel attempts. Tune problem difficulty to achieve a pass rate in the 10–30% range, rewriting scenarios and adjusting parameters as needed. Submit problems for senior review to ensure quality standards. Gain deep understanding of anchor tools and AI model behavior through the iterative calibration and testing process.

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The role involves implementing full stack features end-to-end across front-end, back-end, and cloud infrastructure layers, building and integrating RESTful APIs and cloud-hosted services primarily on Azure, developing front-end components using modern JavaScript/TypeScript frameworks, writing unit, integration, and API tests, using Docker for local development and containerized deployments, and managing work in Git with effective collaboration with AI agents. The engineer will build features that incorporate large language model (LLM) calls via the Claude API or Azure OpenAI, implement retrieval-augmented generation (RAG) components and tool integrations, write evaluation harnesses for LLM-powered features, and document LLM feature behavior clearly. They will participate actively in technical design discussions, use Claude and AI-assisted development tools for prototyping, code generation, and debugging, write clear prompts and specs for AI agents, review AI-generated code critically, and contribute to the development of AI-assisted workflows. Collaboration includes participating in code reviews, working closely with ML engineers, data engineers, and product managers, contributing reusable components to shared libraries, engaging in sprint ceremonies, and proactively seeking feedback to grow technically and toward leadership.

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Design computational engineering problems to challenge a frontier AI model, ensuring the problem has an answer verifiable by code and requires a specialized tool like Cantera, CoolProp, CalculiX, OpenFAST, or others. Each problem runs inside a sealed Linux container with the pre-installed tool and a programmatic judge that grades the model's answer. Tasks include picking an anchor tool and designing a problem that hinges on its solvers, simulation kernels, or domain-specific models, writing a Python reference solution, supplying input files and geometry or mechanism definitions as needed, deciding the numerical answer and tolerance for correctness, testing the problem against the model in batches of parallel attempts, tuning the problem difficulty until the agent only succeeds in a small number of attempts, and submitting tasks to a senior reviewer for feedback and quality assurance. Calibration involves tuning the problem against batches of parallel runs to aim for a pass rate in the 10–30% band, rewriting thermodynamic cycles, tightening material models and boundary conditions, and observing how the agents behave.

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As a Senior Deployment Engineer, AI Agents, you embed with strategic accounts as the technical member of the deployment pod alongside a Deployment Manager and/or Forward Deployed PM. You diagnose and resolve technical blockers such as integration failures, Zendesk configuration issues, automation logic gaps, and data sync problems. You build account-specific automations and integrations that are not yet supported out of the box. You own the technical setup of new accounts during onboarding, ensuring integrations are live, data is flowing, and first automations are configured. You surface patterns across accounts to engineering and product teams to identify recurring technical issues that should become platform features rather than one-off fixes. Additionally, you contribute to shared technical documentation and deployment playbooks that reduce ramp time for future accounts. You also participate in technical scoping during late-stage sales to help the team understand what is realistic before contracts are signed.

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Conceptualise scripts based on current production needs centred around existing AI characters. Create narrative-driven, longform video content, including stylized and explicit NSFW visuals, focusing on storytelling, atmosphere, and visual coherence throughout. Own and manage end-to-end AI video production workflows from ideation, prompting, generation, editing, and post-production. Work extensively with ComfyUI pipelines to build, customize, and optimize node-based workflows for image and video generation. Utilize tools such as Stable Diffusion (AUTOMATIC1111), ComfyUI, Runway, and other AI video platforms to produce high-quality visual sequences. Develop and maintain consistent character appearance, style, and scene continuity across longer narratives using advanced techniques. Integrate motion graphic design and colour correction for cohesive final outputs. Experiment rapidly with new AI models, tools, and techniques, incorporating them into production workflows and sharing skills with the team. Align with Content Lead’s creative direction while maintaining high autonomy in execution and technical decisions. Continuously refine workflows for efficiency, scalability, and output quality.

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You will be responsible for the evaluation tests (evals) used to ensure that the AI engineer Archie is learning and retaining the necessary skills to perform engineering work and to benchmark it against industry skill expectations. Your tasks include clearly defining, implementing, and validating these evals with input from engineering experts and industrial partners. You will translate these evaluation tests into multiple formats for use with different types of AI and non-AI systems and agents. Additionally, you will implement and operate the system for organizing, transforming, running, grading, and reporting on evaluation benchmarks. You will design and execute the process for developing and quality assuring evals, incorporating contributions from the engineering team, industrial partners, and subject-matter experts. Ensuring that evals run effectively within the CI/CD system and continuously benchmarking the evolving AI platform and experiments is part of your responsibilities. You will create methods to detect and test for common AI quality challenges such as hallucinations, undesirable stochasticity, and regressions. You will serve as a technical leader in the consistent implementation and organization of automated tests across other areas of the technology stacks.

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Design, develop, test, deploy, maintain, and improve scalable, secure, and high-performance backend systems with a focus on high availability, low latency, and cost-effectiveness. Act as the subject matter expert in infrastructure when designing new products and introducing new technology to existing products. Collaborate closely with engineering and research teams to integrate infrastructure components with product features to optimize system performance and user experience. Design event-driven architectures and develop APIs and microservices for real-time processing and analytics. Ensure system reliability, performance, and scalability through monitoring, logging, and error handling. Stay current with emerging trends, technologies, and methodologies to enhance infrastructure capabilities. Participate in code reviews, contribute to open-source projects, and mentor junior engineers.

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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.

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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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Collaborate with senior developers to design, build, and test software solutions for the agricultural sector. Contribute to coding, debugging, and documenting software applications. Assist in the development of new features from concept to deployment. Assist in research and development to improve the AI powered Assistant. Help optimize and improve the performance of existing software systems. Participate in code reviews and contribute to development process improvements and best practices. Test software functionality and troubleshoot issues. Collaborate with cross-functional teams such as product managers and data scientists to align technical solutions with business objectives. Stay updated with trends and technologies in software development and agriculture.