Job Details

Data Science – Machine Learning / Full Time / Remote

A mission-driven company focused on enabling equitable and efficient access to insurance for everyone. The goal is to empower insurers to offer affordable coverage to individuals and organizations facing challenges such as climate change, cyber risks, and social inflation. Backed by leading investors, they leverage cutting-edge AI/ML technology to redefine how risks are managed.

Their AI-driven platform uses deep reinforcement learning to help insurance companies optimize their risk portfolios, ensuring fair and equitable pricing in hard-to-price areas. By operationalizing underutilized data and providing real-time risk insights, their solution improves underwriting decisions and empowers insurance underwriters to achieve their goals while delivering an essential service to society.

What You'll Be Doing

• Design and implement scalable machine learning pipelines, optimizing prompt engineering workflows to improve accuracy and efficiency for various insurance use cases.

• Evaluate and benchmark open-source large language models (LLMs), selecting and fine-tuning the most effective ones to meet business needs while ensuring adaptability and innovation.

• Research and incorporate the latest advancements in prompt engineering and model optimization to refine prompts for precision and relevance, contributing to a robust ML infrastructure.

• Collaborate across teams, serving as a technical lead and mentor for junior team members, enhancing overall team performance and knowledge.

• Ensure production-grade deployment standards with a focus on scalability, reliability, and compliance with insurance data handling regulations, balancing rapid iteration with stability.

Who We Hope You Are

• At least 5 years of experience as a Machine Learning Engineer or in a similar role, with a focus on LLM pipelines for the past 3 years.

• Proven experience designing, training, benchmarking, and fine-tuning machine learning models, particularly NLP models and large language models (LLMs); familiarity with open-source models is a bonus.

• Hands-on expertise in building scalable ML pipelines using tools like Kubeflow, along with automating and monitoring ML workflows to maintain consistent model performance in production.

• Experience deploying models on cloud platforms, managing cloud resources, and leveraging relevant APIs for data intake, storage, and processing.

• Strong communication skills with the ability to effectively convey complex findings to non-technical audiences.

Keywords:

Senior Machine Learning Engineer, Remote, AI, Machine Learning, Deep Reinforcement Learning, Large Language Models, LLMs, NLP, Prompt Engineering, Scalable Pipelines, ML Infrastructure, Kubeflow, Cloud Platforms, Model Optimization, Underwriting, Insurance Data, Risk Management, Data Science, Benchmarking, Fine-Tuning, Mentorship, Compliance, Real-Time Insights, Production Deployment, Automation, RiskOps.