Staff Machine Learning Engineer

Federato

4d•$210,000 - $250,000

About The Position

There's nothing more exciting than transforming an industry that's been stagnant for decades. Federato is an AI-native platform that’s bringing agentic AI to the full policy lifecycle. We’re aggressively transforming how insurance work gets done, and the world is paying attention: we've raised $180 million, including a $100 million Series D from Goldman Sachs. We have powerful product-market fit, and we're growing fast globally. You'll get to work on complex problems with one of the most advanced AI teams you'll find anywhere. If you're the person we need, you know that AI bolted onto legacy systems is too weak to matter. That's why we built our platform to be AI-native from day one. That's why you can do here what you can never do at a legacy software company. move fast and prove it, not theorize it. We think from first principles. You’ll work on problems that matter, building software that fundamentally changes how insurance operates.

Requirements

Proven experience as a Machine Learning Engineer or similar role (at least 8 years), with a strong focus on leveraging LLM models over the last 2 years.
Expertise designing scalable and robust machine learning pipelines, both for classical machine learning systems and large language model applications.
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.

Responsibilities

Designing and implementing efficient and scalable machine learning pipelines, across multiple insurance use cases.
Collaborating cross-functionally, serving as a technical lead for junior team members, providing mentorship and guidance to elevate team performance and technical knowledge.
Ensuring production-grade deployment standards, emphasizing scalability, reliability, and compliance with insurance data handling policies, balancing rapid iteration with stability.
Building reusable, modular infrastructure components and CI/CD pipelines for ML and LLM workloads, enabling rapid experimentation and seamless transition from research to production.
Championing best practices in observability, testing, and monitoring of ML systems, establishing standards for model/data drift detection, logging, and automated rollback strategies.