• 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.

• 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

Role Description The Machine Learning Engineer will design, develop, deploy, and maintain advanced machine learning models and data analysis systems to support specialized domain modeling and proprietary feature engineering. The person in this role will: - Conduct applied research and experimentation to design, train, evaluate, and refine machine learning models, including performing feature engineering, selecting modeling techniques, validating model performance, and documenting analytical methods. - Develop, test, and deploy production machine learning systems by managing the complete MLOps lifecycle, including experiment tracking, model versioning, containerization, orchestration of automated workflows, and monitoring of model performance in production environments. - Maintain and optimize machine learning infrastructure to support training, inference, and data processing workflows, including configuring cloud compute environments, tuning distributed computation jobs, and improving system efficiency and scalability. - Collaborate with business stakeholders to translate analytical requirements into quantifiable modeling objectives, define evaluation criteria, validate assumptions with data, and communicate analytical findings and modeling results. - Design, prepare, and review technical documentation, including model design specifications, architecture diagrams, data-flow documentation, and systems integration requirements to support maintainability and long-term scalability. - Develop AI-driven automation solutions using Large Language Models to streamline internal workflows, design LLM-based agentic processes, validate automated outputs, and measure accuracy and efficiency improvements. - Design and develop internal software tools and backend services that support data quality, enable analytical workflows, expose model insights to internal teams, and integrate with organizational data systems and APIs. No travel is required. Fully remote position (100%) from anywhere in U.S. reporting to HQ in San Francisco, CA. Qualifications - Master’s degree (or foreign equivalent) in data science, economics, mathematics or computer science. - 1 year of experience in any occupations in which required experiences were acquired (may be pre-Master’s). Requirements - 1 year of experience designing, training, and evaluating machine learning models using Python-based data science libraries, including experience performing feature engineering and developing predictive and descriptive models using tools such as scikit-learn and pandas. - Experience using machine-learning lifecycle tools, including MLflow (or similar platforms) for experiment tracking, reproducible training workflows, and model versioning. - Experience using Docker for containerization to package machine learning pipelines and ensure reproducible deployment environments. - 1 year of experience orchestrating data processing and machine learning workflows, including scheduling, monitoring, and managing dependencies using Apache Airflow. - 1 year of experience using CI/CD tools to automate model training, testing, and deployment processes. - 1 year of experience configuring and optimizing cloud compute environments to support training, inference, and large-scale data processing tasks. - 1 year of experience developing AI-driven automation workflows using language models, including integrating language-based model components into analytical or operational processes. - Experience developing backend services and APIs using FastAPI, REST, or GraphQL to support data access, model serving, and analytical tooling. - 1 year of experience working with SQL databases, including PostgreSQL, and cloud data platforms (e.g., Snowflake), including writing analytical queries and implementing data-quality validation workflows. - Experience using distributed data-processing tools, including Spark, for large-scale data transformation and feature-engineering operations. Benefits - Salary offered: From $196,776 per year. - Job location: San Francisco, CA. To Apply Send resume to: Eryn@alt.xyz (write “Machine Learning Engineer” in subject line).

• Build Production-Grade Evaluation Systems: Design and implement comprehensive evaluation frameworks that measure agent performance, track improvements over time, and ensure our AI systems deliver consistent value to customers • Drive Experimentation-to-Production Pipeline: Own the entire ML lifecycle from prototype to production, building scalable systems that enable rapid iteration while maintaining reliability and performance in customer environments • Enable Cross-Team ML Integration: Work closely with product teams to seamlessly integrate ML capabilities into customer-facing features, ensuring technical excellence translates into user value and product differentiation • Optimize AI Agent Performance: Continuously improve our AI agents through systematic experimentation, prompt engineering, and architectural enhancements, measuring success through customer impact and system performance • Scale ML Infrastructure: Build the foundational ML systems, monitoring, and tooling that will support our growth from startup to scale, ensuring we can deploy new capabilities quickly without compromising quality • Partner with Engineering Leadership: Collaborate directly with our CTO through regular check-ins and strategic alignment while operating with high autonomy and self-direction in day-to-day execution • Mentor Through Excellence: Provide natural mentorship to junior ML engineers through code reviews, technical guidance, and sharing practical experience from building production ML systems