Forward Deployed Machine Learning Engineer

Federato is an AI-native insurance technology company that helps insurers improve decision-making and manage the full policy lifecycle. The Forward Deployed Machine Learning Engineer will build, deploy, and iterate on machine learning models and agentic workflows for customer-specific insurance use cases, while monitoring production performance and resolving issues. The role partners with Data Science, Engineering, and customers to deliver reliable, high-impact solutions.

Responsibilities:

• Work directly on building, deploying, and iterating on machine learning models and agentic workflow features that address real customer needs
• Apply ML techniques to improve accuracy and overall system performance, ensuring solutions are robust, reliable, and production-ready for customers
• Improve, implement, and validate ML models and agentic workflows supporting submission intake, underwriting decision-making, and automation tasks
• Deploy and adapt autonomous agent behaviors into customer-specific workflows, translating core AI capabilities into practical solutions
• Develop and maintain evaluation pipelines, monitoring systems, and performance metrics to ensure reliability under evolving production conditions
• Monitor production systems via logs, metrics, and user feedback to diagnose issues, debug failures, and drive resolution
• Take end-to-end ownership of problems — implementing fixes or coordinating with engineering and infrastructure teams as needed
• Partner closely with Data Science and Engineering teams to iterate quickly and deliver high-impact solutions

Requirements:

• Bachelor's or master's degree in Mathematics, Operations Research, Data Science, Artificial Intelligence, or a related field with foundational knowledge in machine learning, deep learning, and natural language processing
• Experience working in a fast-paced, cross-functional environment
• 2+ years of experience as a Machine Learning Engineer, Applied Scientist, or similar role delivering ML solutions in production
• Experience working directly with customers or stakeholders to translate business needs into technical solutions
• Hands-on experience adapting, extending, and deploying ML/LLM systems (including agentic workflows and prompt engineering) in real-world use cases
• Strong experience with experimentation, evaluation, and monitoring pipelines, including analyzing production logs and debugging systems
• Experience deploying and iterating on ML systems in cloud environments in collaboration with engineering teams
• Proven track record of ownership — driving issues through to resolution in production systems