Forward Deployed Machine Learning Engineer

Federato; San Francisco (CA); On-site; USD 155,000 - 180,000; Full time

Benefits offered by this job

Stock options

Bonus

Job summary

Federato, an AI-native platform in San Francisco, seeks a Machine Learning Engineer to build, deploy, and iterate on ML models and agentic workflow features that address real customer needs.

You will collaborate with Data Science and Engineering teams to translate business requirements into practical, production-ready ML solutions, monitor performance, and ensure reliability while delivering high-impact results.

Qualifications

• Bachelor's or master's degree in Mathematics, Operations Research, Data Science, AI or related field.
• Hands-on experience delivering ML solutions in production environments.
• Experience translating business needs into technical ML solutions.
• Experience with ML/LLM systems and prompt engineering.

Responsibilities

• Build, deploy, and iterate on ML models and agentic workflow features addressing real customer needs.
• Apply ML techniques to improve accuracy and system performance for production readiness.
• Improve, implement, and validate ML models and workflows for submission intake, underwriting, and automation.
• Deploy autonomous agent behaviors into customer workflows and translate AI capabilities into practical solutions.
• Develop evaluation pipelines, monitoring systems, and performance metrics for reliability.
• Monitor production systems, diagnose issues, and drive resolution.
• Take end-to-end ownership of problems and coordinate with eng/infra teams as needed.
• Collaborate with Data Science and Engineering teams to iterate quickly and deliver impact.

Skills

Machine Learning

MLOps

Python

LLM systems

Prompt engineering

Production ML

Data analysis

Education

Bachelor's or Master's in related field

Tools

AWS

GCP

Job description

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

• Work directly on building, deploying, and iterating on machine learning models and agentic workflow features that address real customer needs
• Apply ML techniques to improve accuracy and overall system performance, ensuring solutions are robust, reliable, and production-ready for customers
• Improve, implement, and validate ML models and agentic workflows supporting submission intake, underwriting decision-making, and automation tasks
• Deploy and adapt autonomous agent behaviors into customer-specific workflows, translating core AI capabilities into practical solutions
• Develop and maintain evaluation pipelines, monitoring systems, and performance metrics to ensure reliability under evolving production conditions
• Monitor production systems via logs, metrics, and user feedback to diagnose issues, debug failures, and drive resolution
• Take end-to-end ownership of problems - implementing fixes or coordinating with engineering and infrastructure teams as needed
• Partner closely with Data Science and Engineering teams to iterate quickly and deliver high-impact solutions

Who We Hope You Are

• Bachelor\'s or master\'s degree in Mathematics, Operations Research, Data Science, Artificial Intelligence, or a related field with foundational knowledge in machine learning, deep learning, and natural language processing.
• Experience working in a fast-paced, cross-functional environment
• 2+ years of experience as a Machine Learning Engineer, Applied Scientist, or similar role delivering ML solutions in production
• Experience working directly with customers or stakeholders to translate business needs into technical solutions
• Hands-on experience adapting, extending, and deploying ML/LLM systems (including agentic workflows and prompt engineering) in real-world use cases
• Strong experience with experimentation, evaluation, and monitoring pipelines, including analyzing production logs and debugging systems
• Experience deploying and iterating on ML systems in cloud environments in collaboration with engineering teams
• Proven track record of ownership - driving issues through to resolution in production systems

Base Salary Range: $155,000 - $180,000

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 also includes bonus, 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 [e-mail]