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:
- Design and implement scalable machine learning pipelines, serving prompt engineering workflows to enhance scalability and efficiency in submission intake processes across multiple insurance use cases.
- Collaborate cross-functionally, serving as a technical lead for mid and senior 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.
- Build reusable, modular infrastructure components and CI/CD pipelines for ML and LLM workloads, enabling rapid experimentation and seamless transition from research to production.
- Champion best practices in observability, testing, and monitoring of ML systems, establishing standards for model/data drift detection, logging, and automated rollback strategies.
- Partner with Data Science leaders to shape the vision and direction of MLOps at Federato and the future of our products
Who We Hope You Are:
- Proven experience as a Machine Learning Engineer or similar role (at least 7 years), with a strong focus on pipelining LLM models over the last 2 years.
- Proven experience in designing scalable and robust machine learning pipelines, both for classical machine learning 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.
- Experience leading a team for high-visibility / high-impact projects
- Proven record of influencing and executing ML product vision
$210,000 - $250,000 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 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. Our ability to make a difference starts with our people. We would love to work with you!
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 [email protected]
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