• Contribute to the development and implementation of frameworks to evaluate and monitor the innovative machine learning solutions at Workiva • Assist in building the platform and metrics to evaluate and govern the ML/GenAI based solutions • Support the development of tools, systems, infrastructure, and automation to evaluate the performance and monitoring of applications • Work closely with senior team members to troubleshoot issues related to accuracy, safety latency of ML based solutions • Gain hands-on experience with Workiva’s technical standards and methods, while taking ownership of assigned activities

Role Description The Staff Software Engineer is responsible for leading a team of engineers building and designing a product that our customers and associates love. As a Staff Software Engineer, you will be part of a dynamic team with engineers of all experience levels who help each other build and grow technical and leadership skills while creating, deploying, and supporting production applications. In this role, you will also provide technical leadership on machine learning systems, including: - Model development - Production deployment - Monitoring - Lifecycle management of ML solutions operating at scale Staff Software Engineers will assist in: - Product and tool selection - Configuration - Security - Resilience - Performance tuning - Production monitoring They contribute to foundational code elements that can be reused as well as architectural diagrams and other product-related documentation. You will help define best practices for building reliable, explainable, and maintainable ML systems that integrate seamlessly with broader software platforms. As a Staff Software Engineer, you will be a core player on the product team and are expected to build and grow the skillsets of the more junior Engineers. Qualifications - 3 - 6 years of relevant work experience - Strong experience designing, training, evaluating, and deploying machine learning models in production environments - Experience with ML lifecycle management - Experience building and operating ML pipelines using cloud-native services - Strong understanding of applied statistics and model evaluation metrics - Experience with algorithms such as clustering, forecasting, anomaly detection, and neural networks - Experience in advanced machine learning techniques such as NLP, convolutional neural networks, and embeddings generation - Experience in training machine learning models with extremely large datasets - Experience with Data Analysis and Machine Learning Tools and Libraries - Experience in Google Cloud Platform and AI/ML related components - Experience in effective data engineering practices and big data platforms - Experience in a modern scripting language (preferably Python) - Experience in writing SQL queries against a relational database - Experience in version control systems (preferable Git) - Experience in a Linux or Unix based environment - Experience in a CI/CD toolchain - Experience in REST and effective web service design - Experience in production systems design - Experience in cloud computing platform and associated automation patterns - Experience in defensive coding practices and patterns for high Availability - Experience in A/B testing and effective REST design - Familiarity with advanced machine learning architectures Requirements - Must be eighteen years of age or older - Must be legally permitted to work in the United States Benefits - Typically requires overnight travel 5% to 20% of the time - Most of the time is spent sitting in a comfortable position with frequent opportunities to move about - Located in a comfortable indoor area Company Description The pay range for this position is between $120,000.00 - $190,000.00 for California, Colorado, Connecticut, Rhode Island, Nevada, New York City, Ithaca (NY), Westchester County (NY), and Washington residents.

• Establish engineering standards, best practices, and evaluation frameworks for AI systems • Lead technical decision-making for model selection, system design, and deployment strategies • Act as the subject matter expert for agentic AI and modern LLM-based systems within the organization • Architect and deliver production-grade, multi-step AI agents capable of autonomous reasoning, tool orchestration, task decomposition, memory management, and human-in-the-loop escalation • Design and deliver AI systems on enterprise cloud platforms (e.g., AWS, Azure), including LLM services (AWS Bedrock, Azure OpenAI) • Own the agent evaluation and observability stack, including benchmarking, tracing, regression testing, and performance monitoring • Optimize LLM inference costs and resource utilization for production workloads • Partner with business leaders to identify, prioritize, and shape AI-driven initiatives aligned with organizational goals • Translate complex business problems into scalable AI solutions with measurable impact • Drive roadmap planning and investment decisions related to AI and automation • Collaborate with IT, data engineering, and operations teams to integrate AI solutions into enterprise systems • Mentor and develop machine learning engineers and data scientists • Provide technical guidance and elevate team capabilities in modern AI practices • Ensure responsible and compliant use of AI systems, including managing risks related to model behavior, data usage, and regulatory considerations in a highly regulated industry • Lead evaluation and integration of external AI platforms and vendors, including assessment of cost, intellectual property, scalability, security, and long-term architectural impact

• Você será responsável pela sustentação da plataforma de automação n8n em modelo self-hosted sobre a infraestrutura da Amazon Web Services, garantindo disponibilidade, segurança, performance e atualização do ambiente. • Também atuará na administração e operação do AWS EKS, realizando rotinas de manutenção, controle de versões, ajustes de capacidade e suporte às aplicações executadas no cluster. • Faz parte das suas atribuições o desenvolvimento, evolução e sustentação de pipelines de CI/CD, apoiando os times de ciência de dados na entrega contínua, padronização de esteiras e automação de deploys. • Será igualmente responsável pela implementação e manutenção de infraestrutura como código (IaC) utilizando Terraform, garantindo versionamento, reprodutibilidade e governança dos ambientes. • Por fim, atuará fortemente em troubleshooting em ambiente cloud, investigando incidentes, falhas de pipeline, problemas de rede, acesso, performance e integração entre serviços, participando ativamente da melhoria contínua da plataforma e da confiabilidade dos ambientes produtivos.