Forward Deployed Machine Learning Engineer

Federato is on a mission to defend the right to efficient, equitable insurance for all, leveraging AI to transform the insurance industry. The Forward Deployed Machine Learning Engineer will build and deploy machine learning models, improve system performance, and work closely with Data Science and Engineering teams to deliver impactful 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