Staff Machine Learning Engineer
- Role
- AI / ML
- Experience
- Lead
- Employment
- Full-time
- Company size
- Mid-size
The listing doesn't say where it hires from. It may hire in United States (90% confidence). This is an estimate, not an eligibility rule; verify before applying.Signals: employment terms and salary level.
No BS summary
Senior ML engineer (Staff level) with 8+ years experience and 2+ years working with LLMs. Build and productionize scalable ML and LLM pipelines, CI/CD, observability, and compliance for insurance data. Remote role with compensation $210,000–$250,000 annually.
Core skills
Required skills
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. What You’ll Be Doing: 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. What We Hope You Bring: 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. Our cash compensation amount for this role is $210,000 to $250,000 annually. 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 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 talent@federato.ai
What you'll do
- Designing and implementing efficient and scalable machine learning pipelines across multiple insurance use cases.
- Collaborating cross-functionally and serving as a technical lead for junior team members, providing mentorship and guidance.
- Ensuring production-grade deployment standards with emphasis on scalability, reliability, and compliance with insurance data handling policies.
- Building reusable, modular infrastructure components and CI/CD pipelines for ML and LLM workloads to enable rapid experimentation and transition from research to production.
- Championing best practices in observability, testing, and monitoring of ML systems, including model/data drift detection, logging, and automated rollback strategies.
What they require
- Proven experience as a Machine Learning Engineer or similar role (at least 8 years).
- Strong focus on leveraging LLM models over the last 2 years.
- Expertise designing scalable and robust machine learning pipelines for classical ML and LLM 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.
Benefits
- Total compensation package includes stock options, benefits and additional perks.
- Equal-opportunity employer and reasonable accommodation for individuals with disabilities (contact talent@federato.ai).
Federato is an AI-native platform for insurers that spans the full policy lifecycle, helping underwriters triage submissions, get portfolio feedback, and consolidate workflows.