Technical · Fully Remote Senior Machine Learning Engineer / Tech Lead - AI & ML Are you up to the challenge of building the next generation of Machine Learning products on the cloud with us? We have an international, fully remote team working on creating the best Kubernetes-first, innovative virtualization platform around, and we'd like you to join as a senior engineer and team lead. In this role, you will be bringing new products to life, working with cutting-edge technology, and making a daily difference supporting users by giving them a platform on which to build awesome new things. You will get fully involved with the inner workings of Artificial Intelligence, Kubernetes, virtualization and cloud computing, with opportunities to contribute back to open-source projects and participate at industry conferences around the world. You won't just ship code: you'll join a supportive team that values your perspective and ideas from day one. Responsibilities: - Manage the development and maintenance of scalable, efficient code for the Machine Learning components of the Civo Cloud Platform - Ensure code quality, performance, and reliability through comprehensive testing and optimization - Collaborate with designers, product managers, and other engineers to translate requirements into technical solutions - Improve team performance through implementing best practices, refactoring, optimizing and improving the documentation of code and the application of best practices - Participate in code reviews, providing constructive feedback and fostering a collaborative environment - Stay up-to-date with the latest Machine Learning trends, libraries, and tools - Troubleshoot and debug complex technical issues Requirements: - Bachelor’s degree in Computer Science, Engineering or a related field, or proven related professional experience - 4+ years of professional experience developing, deploying and optimising Machine Learning Solutions - 2+ years of running large applications and systems in production environments - 2+ Experience with containerization technologies (Docker, Kubernetes) - Proven Experience training Machine Learning Models of any modality - Experience building and designing Machine Learning Pipelines or workflows (MLOps) - Experience managing complex Kubernetes Deployments - Experience scaling GPU Machine learning deployments and managing GPU Clusters - Strong written and verbal communication skills - Experience leading complex software development projects Nice to Have: - Experience in a software development management or leadership roles - Experience using Machine Learning monitoring tools - Experience enforcing coding standards and practices - Experience working in asynchronous agile software teams - Experience working in fully remote organizations - Experience with open source Machine Learning projects Why Join Civo: - Competitive compensation and benefits package - 4-day week company (unless attending an event) - Uncapped holiday - Collaborative and inclusive culture that values diversity and creativity - Opportunity to work with a dynamic and innovative team in the fast-growing cloud industry Department Technical Remote status Fully Remote

• Projetar e implementar pipelines de validação de dados e avaliação de modelos em ambiente cloud (AWS); • Integrar dados e métricas de avaliação em fluxos automatizados e auditáveis; • Modularizar pipelines para facilitar reuso, manutenção e escalabilidade; • Trabalhar em parceria com times de Data Science, Engenharia de Dados e Produto; • Garantir boas práticas de versionamento, logging, monitoramento e testes automatizados; • Propor melhorias contínuas em arquitetura de dados e processos de validação; • Apoiar deploy e acompanhamento de modelos em produção; • Atuar na análise de performance, drift e custos dos modelos implementados.

• Design, construct, and manage robust data pipelines for the training, validation, and continuous retraining of Large Quantitative Models (LQMs) and agentic frameworks. • Develop, implement, and rigorously test novel ML models and algorithms, defining appropriate metrics to ensure model performance aligns with high-level product objectives. • Contribute to the efforts in cleaning, transforming, and engineering features from complex and large-scale datasets to optimize LQM performance and predictive accuracy. • Conduct deep analysis of model behavior, performance, and failure modes, tuning hyper-parameters and optimizing model architecture for efficiency, speed, and accuracy in a production context. • Collaborate closely with AI researchers, product managers, and SWEs to translate high-level business objectives into actionable ML development and deployment roadmaps. • Champion and enforce exceptional engineering standards for code quality, system efficiency, and security in a prototyping environment.

• End-to-End Product Engineering: Rapidly prototype and scale full-stack applications, ensuring seamless integration between UI, business logic, and ML inference. • Architectural Leadership: Design modular, extensible system architectures that support rapid iteration without accruing technical debt. • Technical Governance: Lead code reviews across the stack and serve as escalation point for engineering, infrastructure, and security decisions. • Risk & Remediation: Identify scalability bottlenecks and security vulnerabilities and present remediation strategies. • Bridge Research & Product: Translate experimental ML techniques into production-ready features. • Infrastructure & Compliance: Design infrastructure aligned with government audit requirements (e.g., HIPAA, FedRAMP). • Security-First Development: Implement IAM and secure data practices across the lifecycle. • AI-First Product Ownership: Drive conception and execution of AI-first products, maintaining a strong bias toward applying state-of-the-art capabilities in production environments.