AI Engineer

• Be the subject matter expert powering AI enablement across the business.
• Build a library of proven use cases: audit automation, tax research, advisory insights, and more.
• Lead our AI user community: Q&A, show-and-tell sessions.
• Track adoption and impact, and report results to executive leadership.
• Build AI agents and workflows that solve real business problems.
• Keep everything aligned to governance, risk and responsible AI standards.

AI & Digital Tools Adoption Lead

• Support the development of AI/ML requirements
• Represent the client at AI/ML related Defence activities (conferences and working groups)
• Develop AI/ML policy for the certification to support future UAS projects within the client's program of work including RAS at Scale Delivery Model

Artificial Intelligence / Machine Learning Engineer

• Build customer-facing and internal product features end to end.
• Develop web flows, backend APIs, admin tools, dashboards and operational systems.
• Support products across insurance, payments, savings, investing, travel and financial services.
• Work with product, design, mobile and operations teams to launch fast.
• Integrate APIs, handle data flows and improve production reliability.
• Use analytics, feedback and production issues to improve features after launch.
• Help build tools that reduce manual work and improve speed across the business.

Full Stack Software Engineer – AI Neobank App

• Build AI agents, workflows, tools and automations for financial services
• Apply LLMs and automation to practical workflows
• Create AI-assisted experiences for various operations
• Work with product and engineering teams to quickly build products
• Design around key constraints like accuracy and security
• Turn manual processes into AI-enabled systems

Applied AI Engineer

• Build AI-powered workflows, assistants, agents and automation systems.
• Apply AI across customer support, CRM, onboarding, claims, renewals, payments, operations and internal tools.
• Work with product and engineering teams to turn manual processes into scalable AI-native systems.
• Build integrations with LLMs, internal data, APIs, documents, knowledge bases and business systems.
• Design evaluation, monitoring and fallback flows so AI outputs are useful, safe and reliable.
• Prototype quickly, test with users or operators, then productionize what works.
• Improve speed, quality and consistency across workflows using AI where it creates real business value.

Applied AI Engineer – AI Neobank App

• Build and ship Android features using Kotlin.
• Create clean mobile flows for insurance, payments, claims, renewals and financial services.
• Work with product and design to simplify complex user journeys.
• Integrate backend APIs and ensure mobile flows are stable, secure and reliable.
• Improve app performance, crash rate, loading states, responsiveness and memory usage.
• Use analytics, user behaviour and production issues to improve the product.
• Build AI-assisted mobile experiences only where they genuinely improve the user journey.

Mobile Developer – AI Neobank App

Applied AI Engineer – Finance Super App

• Own the end‑to‑end learning design for priority AI learning workstreams, including AI Foundations, role‑based learning and immersive learning experiences across the firm.
• Translate business and capability needs into intentional learning journeys aligned to agreed AI skills frameworks.
• Design and build high-quality learning assets (eLearning, facilitator guides, toolkits and communications) that scale consistently across the firm.
• Partner with Change and business stakeholders to enable learning communities (e.g., AI Champions) with practical resources, facilitation support and upskilling that reinforce learning in the flow of work.
• Lead learning experience (LX) design to ensure journeys are coherent, intuitive, and focused on application and impact — not just awareness.
• Facilitate selectively and coordinate delivery of virtual and in‑person learning experiences, partnering with SMEs and facilitators to ensure quality and relevance.
• Own governance rhythms for assigned learning workstreams, including working groups, reporting, risk/issue tracking and stakeholder updates.
• Partner with Risk & Responsible AI, Firmwide Technology and LLTD teams to ensure content reflects current tools, policies and risk expectations.
• Define and track meaningful learning success measures (participation, feedback, application) and drive continuous improvement through iteration and evidence‑based insights.

AI Learning Experience & Design Lead

• Join a company dedicated to connecting people and technology
• Work on impactful digital technology solutions with a diverse software engineering team
• Contribute to customer requirements through technical design and integration solutions

Senior MuleSoft Developer – AI

Machine Learning Engineer

• You'll design, implement, and refine machine learning models to deliver personalized recommendations, taking on the most complex and ambiguous parts of the system.
• You'll improve the architecture, code structure, and performance of our machine learning systems, helping raise the engineering quality bar across the team.
• You'll investigate research papers and state-of-the-art machine learning models, and judge which ones are worth bringing into production at Canva.
• You'll lead the design of online and offline experiments to validate model performance, and make data-driven calls on what to ship and what to iterate on.
• You'll shape how our machine learning models integrate with the broader technology stack, ensuring strong performance and reliability across the pipeline.
• You'll work closely with product and engineering teams to deploy new recommendation features, driving alignment when initiatives span multiple teams.
• You'll document and communicate your work to both technical and non-technical stakeholders — including senior leadership — and mentor MLEs around you through reviews, pairing, and shared knowledge.

Staff Machine Learning Engineer – Recommendations

• Act as a solution expert across ML domains including evaluations, training, inference, data pipelines, quality, and optimisation
• Work directly alongside product teams as a trusted partner, helping them navigate technical challenges and arrive at effective solutions
• Provide expert guidance on platform capabilities, helping teams understand what's possible and how to get there efficiently
• Develop blueprints, patterns, and paved roads that allow other teams to follow proven approaches and accelerate their own implementations
• Debug and resolve complex issues, identifying root causes and sharing learnings broadly
• Balance hands-on problem solving with strategic thinking about how to scale enablement impact across the organisation

Senior Machine Learning Engineer – VSP Authoring & Distribution

AI Research Scientist

• Act as a subject matter expert supporting the development and improvement of AI solutions in the regulatory compliance market
• Validate AI-generated responses
• Identify areas for improvement
• Conduct legal and regulatory research
• Help structure content
• Review and validate AI-generated legal and regulatory responses for accuracy, relevance and usability
• Identify logic gaps, edge cases and performance issues, and recommend improvements
• Provide structured feedback to product, engineering and data science teams
• Support prompt design, evaluation frameworks and training data improvements
• Identify patterns and insights to enhance AI performance and customer experience
• Contribute to taxonomy, metadata and content structuring for AI systems
• Investigate customer feedback and determine optimal solutions
• Work across Australian, UK and global regulatory environments

Legal AI Researcher/Analyst – Regulatory Compliance II

• Review AI-generated outputs related to meditation, wellbeing, mental health, sleep, anxiety, stress, and behavior change.
• Score and label AI outputs using structured rubrics and evaluation frameworks.
• Assess whether outputs are clear, safe, evidence-informed, clinically appropriate, and useful for real-world users.
• Check claims against available evidence, including academic literature, clinical guidance, and internal research standards.
• Identify recommendations that are unsafe, overstated, unsupported, vague, misleading, or poorly explained.
• Flag potential issues related to indications, contraindications, evidence quality, risk, and real-world applicability.
• Compare AI outputs across different prompts, workflows, models, or evaluation conditions.
• Help prepare short research summaries, evidence notes, and internal review documents.
• Support prompt testing and AI evaluation workflows, including documenting what works, what fails, and why.
• Use tools such as ChatGPT, Claude, and other AI systems to support research, synthesis, review, and day-to-day work.
• Communicate structured feedback to the Head of AI, Clinical AI Research Lead, product team, and engineers.
• Help improve the quality, safety, and usefulness of AI-powered wellbeing experiences over time.
• Support internal research and publication-related work where relevant, under supervision.

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