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’s AI/ML-driven platform leverages deep reinforcement learning to help insurance companies optimize the portfolio of risks they insure, allowing them to continue to provide fair and equitable pricing in difficult-to-price areas. Our category-defining ‘RiskOps’ solution drives better underwriting decisions by operationalizing underutilized data investments and surfacing real-time risk and portfolio insights. We focus on putting insurance underwriters back in the driver’s seat, helping them meet their goals while providing an important service to society.
What You'll Be Doing
Design and implement scalable machine learning pipelines, optimizing prompt engineering workflows to enhance accuracy and efficiency in submission intake processes across multiple insurance use cases.
Evaluate and benchmark open-source large language models (LLMs), selecting and fine-tuning the most effective ones to address business-specific requirements while maintaining an eye on adaptability and future innovation.
Continuously research and incorporate the latest advancements in prompt engineering and model optimization to refine prompts for precision and relevance, contributing to a robust, cutting-edge ML infrastructure.
Collaborate cross-functionally, serving as a technical lead for junior team members, providing mentorship and guidance to elevate team performance and technical knowledge.
Ensure production-grade deployment standards, emphasizing scalability, reliability, and compliance with insurance data handling policies, balancing rapid iteration with stability.
Who We Hope You Are
Proven experience as a Machine Learning Engineer or similar role (at least 5 years), with a strong focus on pipelining LLM models over the last 3 years.
Proven experience in designing, training, benchmarking, and fine-tuning machine learning models, particularly with NLP models and large language models (LLMs) along with familiarity with open-source models is a plus.
Experience in building scalable ML pipelines using tools such as Kubeflow. Knowledge of automating and monitoring ML workflows to ensure consistent model performance in production.
Hands-on experience with cloud platforms, including deploying models, managing cloud resources, and using relevant APIs for data intake, storage, and processing
Great communication skills with the ability to convey complex findings to non-technical audiences.
Here at Federato, your capabilities are important, but culture fit is quintessential. We move fast, are eager to listen to our users, take a first principles approach to solving problems, and value learning and the ability to change our minds. Most importantly, we're here to have fun, so sticks-in-the-mud need not apply. We are an equal-opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender expression, sexual orientation, age, marital status, veteran status or disability status. We will provide reasonable accommodation to individuals with disabilities to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation at talent@federato.ai
