Senior Machine Learning Engineer

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. What You'll Be Doing : Develop, implement, and validate machine learning and agentic flows to improve submission intake workflows, underwriting decision making, and related predictive/automation tasks. Drive innovation in modeling approaches while balancing accuracy, efficiency, and interpretability. Establish, maintain, and evolve the principles guiding autonomous agent behavior and decision-making across AI workflows. Lead the design of benchmarking frameworks, evaluation tools, and metrics to continuously measure, upgrade, and optimize models, LLMs, and related systems for latency, predictive accuracy, and coverage of relevant use cases, ensuring Federato’s AI capabilities are both performant and reliable at scale. Build modular, reusable model architectures, training routines, and evaluation frameworks that enable rapid experimentation and adaptation to multiple insurance use cases. Document and share findings to create repeatable modeling practices. Collaborate with MLEs (MLOps) and DEs to ensure smooth integration and deployment. Lead the research, design, and implementation of novel machine learning models and agentic workflows that drive next-generation product capabilities. Identify opportunities for applying advanced modeling techniques, optimize model architectures for performance, accuracy, and coverage, and collaborate cross-functionally to translate innovations into high-impact, production-ready AI solutions.. Who We Hope You Are: Proven experience as a Applied Scientist or Machine Learning Engineer, or similar role (at least 5 years), with at least 2 years of focused experience designing, developing, and fine-tuning large language models (LLMs) and advanced ML models in real-world applications. Proven experience designing scalable, robust, and reusable ML/LLM model architectures and evaluation frameworks, including both classical machine learning and modern generative models. Familiarity with open-source LLMs and model adaptation techniques is a plus. Hands-on experience with model experimentation, benchmarking, and evaluation pipelines, including monitoring model performance, drift, and generalization across use cases to ensure reliability and reproducibility. Experience integrating models with production systems in collaboration with engineering teams, including deploying, monitoring, and iteratively improving models in cloud or hybrid environments. Excellent communication skills, with the ability to convey complex modeling concepts, trade-offs, and findings to technical and non-technical stakeholders. $185,000 - $215,000 a year Final offer amounts are determined by multiple factors including candidate location, experience and expertise and may vary from the amounts listed above. Total compensation package does include stock options, benefits and additional perks. Here at Federato, your capabilities are important, but culture fit is essential. We move fast, are eager to listen to our users, take a first principles approach to solving problems, and value learning and the ability ... (truncated, view full listing at source)