Senior Machine Learning Engineer

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.

$170,000 - $210,000 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.