AI Research Scientist

• 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.

Clinical AI Research Assistant

AI Engineer

• Contribute to the prototyping and deployment of AI and Generative AI solutions to support client pursuits, sector use cases and demonstrations.
• Develop and test LLM-powered applications and agentic workflows under guidance, including tool or function calling patterns.
• Support engineering of data-driven architectures across cloud and on-premises environments.
• Assist in developing scalable AI systems with embedded Responsible AI and security controls.
• Apply MLOps and LLMOps practices including experimentation tracking, model testing and deployment support.
• Build and maintain data pipelines integrating structured and unstructured data sources.
• Work with vector databases alongside relational and NoSQL systems.
• Support deployment of AI workloads using platforms such as AWS and Azure.
• Collaborate with architects, sector teams and engineers to transition proof-of-concepts toward production-ready solutions.

AI Engineer – Consultant, Senior Consultant

Machine Learning Engineer

• Architect Durable Agent Runtime leveraging agentic AI frameworks
• Build the Integration Fabric allowing agents to discover tools and negotiate capabilities
• Develop the Memory Manager optimizing RAG pipelines and eviction policies
• Create the trust layer with governance ensures agents operate within deterministic boundaries

Senior Machine Learning Engineer

• Contribute to the evolution of Canva’s unified training platform for AI training workloads
• Improve reliability, observability, debugging, and operational support for training systems
• Design and build platform capabilities for better scheduling at scale and resource management
• Collaborate with research scientists, ML engineers, product teams, and cloud/infrastructure teams
• Shape platform roadmap based on user pain points and long-term platform maturity
• Mentor engineers and share best practices in AI systems and infrastructure

Senior Machine Learning Engineer – Training Platform

• Own the technical direction of CTM's shared AI/ML platform
• Lead the engineering function across the CTM Intelligence Pillar
• Hire well into gaps in the pillar
• Create the conditions for the team to do its best work
• Co-lead the internal AI Guild to build AI/ML capability across CTM’s engineering organisation

Principal Engineer, AI Platform

• Rapidly prototype AI use cases such as summarisation, forecasting, and trend detection
• Work closely with stakeholders to understand business challenges and identify potential AI applications
• Analyse data availability, quality, and suitability for proposed AI solutions
• Conduct feasibility studies and risk assessments for AI use cases
• Develop prototypes and proof-of-concepts to validate assumptions and demonstrate potential impact
• Provide clear recommendations on whether to proceed, pivot, or discard use cases based on findings
• Stay updated on emerging AI technologies and advise on their applicability to business needs
• Contribute to internal knowledge sharing and best practices

• Design, build, and maintain scalable systems for serving machine learning models in production
• Optimise inference performance, including latency, throughput, and cost efficiency
• Collaborate with ML researchers and engineers to productionise models
• Implement monitoring, logging, and alerting to ensure system reliability
• Contribute to platform tooling that improves developer experience and ML workflows
• Identify opportunities to improve infrastructure and drive technical enhancements across the platform
• Provide on-call support to the services supported by the team

Machine Learning Engineer – Inference Platform

• Work across the Campaign Monitor product to identify valuable opportunities in product and customer data, and turn them into predictive features that improve customer outcomes
• Turn rich historical product and customer data into predictive features that improve customer outcomes
• Identify high-impact opportunities for applied machine learning by analyzing product, behavioral, and content data, and translating ambiguous product questions into concrete ML use cases
• Develop and deploy predictive machine learning models, including models for click-through rate, churn, recommendations, and related engagement signals
• Design and build features and training datasets from structured product data, historical behavioral data, and content-derived signals
• Own the applied ML lifecycle from data exploration and feature engineering through training, evaluation, deployment, monitoring, and iteration
• Build production services and workflows for batch and real-time inference, with a pragmatic focus on reliability, maintainability, and speed to impact
• Work hands-on in the codebase, contributing to backend systems and product workflows that consume predictions and recommendations
• Partner closely with product, design, and engineering to turn customer needs into ML-driven product capabilities with measurable business impact
• Establish pragmatic best practices for model evaluation, experimentation, monitoring, and continuous improvement
• Help shape how applied machine learning is introduced into the product, while aligning with broader engineering architecture and delivery practices
• Contribute to shared knowledge across the engineering organization to improve understanding and adoption of applied ML over time

Staff Applied ML Engineer

• Work closely with stakeholders to understand business problems and translate them into practical technical solutions
• Design and deliver end-to-end solutions in commercial, enterprise, or consulting environments
• Apply AI and ML tools pragmatically to improve workflows, processes, and outcomes
• Use AI development tools such as Claude to accelerate software delivery and improve productivity
• Design and build practical applications that bring together business needs, software engineering, and AI capability
• Contribute to the development of Azure web applications using .NET and C#
• Move confidently between business context, solution design, and technical delivery
• Bring a flexible, concept-driven approach to solving problems across different domains and client environments
• Contribute to solutions that embed AI into real business settings, rather than focusing on research-led model development
• Learn and apply new technologies quickly, adapting to different challenges and delivery contexts
• Work with guidance from senior leaders while taking ownership of your contribution and delivery outcomes

AI Engineer – Software Engineer

• Design, build, and deploy AI-driven solutions that enhance operational efficiency and support sales performance
• Translate real business problems into scalable, production-ready AI solutions
• Deliver end-to-end AI solutions that improve operational and sales efficiency, integrating them across core systems
• Develop agentic workflows and autonomous components using Python, Langflow, and modern AI tooling
• Own the full solution lifecycle, from requirements gathering and stakeholder engagement through to design, testing, and deployment
• Embed AI-driven components into existing applications to enhance functionality and user experience
• Manage and optimise AI infrastructure, including specialised Kubernetes nodes and model orchestration environments, in partnership with SRE and Platform teams
• Apply best practices across testing, documentation, and monitoring to ensure reliable, high-performing solutions
• Stay up to date with emerging AI trends and tools to continuously improve internal capabilities

• Build and improve data pipelines, dataset tooling, and platform components that support ML training and evaluation
• Partner with ML and applied science teams to translate training/evaluation needs into scalable platform solutions
• Implement quality controls (validation, monitoring, testing) to improve trust in data and pipeline outcomes
• Debug issues across data + ML workflows, driving root-cause fixes and preventing repeat incidents

See all jobs on JobTailor

Clinical AI Research Assistant

AI Engineer – Consultant, Senior Consultant

Senior Machine Learning Engineer

Senior Machine Learning Engineer – Training Platform

Principal Engineer, AI Platform

AI Engineer

Machine Learning Engineer – Inference Platform

Staff Applied ML Engineer

AI Engineer – Software Engineer

AI Engineer

Machine Learning Engineer – Training & Dataset Platform