Forward Deployed Machine Learning Engineer

Federato

About The Position

Federato is on a mission to defend the right to efficient, equitable insurance for all. We enable insurers to provide affordable coverage to people and organizations facing the issues of today - the climate crisis, cyber-attacks, social inflation, etc. Our vision is understood and well funded by those behind Salesforce, Veeva, Zoom, Box, etc. Federato is the only AI-native platform that spans the full policy lifecycle and changes the way insurance work gets done. Better decisioning is built-in, not bolted on: insurers' unique portfolio goals, strategies, rules, and appetite are part of the workflow so underwriters win the right deals, faster. From the moment a submission hits an underwriter’s inbox, AI is put to work, triaging submissions with a focus on high-appetite business, delivering real-time feedback on the portfolio, and consolidating workflows into a single proven system. Federato drives better business outcomes.

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

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