Data Science - Machine Learning / Full Time / Remote Join a mission-driven company dedicated to providing equitable and efficient insurance access for all. We empower insurers to provide affordable coverage to individuals and organizations grappling with challenges such as climate change, cyber risks, and social inflation. Supported by top investors, we utilize cutting-edge AI/ML technology to transform risk management.
Our innovative AI-driven platform employs deep reinforcement learning to assist insurance companies in optimizing their risk portfolios, ensuring fair pricing in challenging markets. By turning underutilized data into actionable insights, we enhance underwriting decisions and support underwriters in delivering crucial services to society.
Your Role and Responsibilities - Design and create scalable machine learning pipelines to optimize prompt engineering workflows, boosting accuracy and efficiency for diverse insurance applications.
- Assess and benchmark open-source large language models (LLMs) to identify and fine-tune the most effective options, ensuring they meet business objectives while fostering innovation.
- Stay updated on the latest developments in prompt engineering and model optimization, refining prompts for enhanced precision and relevance, and contributing to a resilient ML infrastructure.
- Collaborate across teams, acting as a technical lead and mentor to junior members, enhancing both team performance and knowledge sharing.
- Maintain production-grade deployment standards, focusing on scalability, reliability, and adherence to insurance data regulations while balancing quick iterations with stability.
Ideal Candidate Profile - A minimum of 5 years of experience as a Machine Learning Engineer or in a related role, with at least 3 years focusing on LLM pipelines.
- Demonstrated expertise in designing, training, benchmarking, and fine-tuning machine learning models, especially NLP models and LLMs; experience with open-source models is a plus.
- Proficient in creating scalable ML pipelines using tools such as Kubeflow, with hands-on experience in automating and monitoring ML workflows to ensure consistent performance in production.
- Experience in deploying models on cloud platforms, managing resources effectively, and utilizing relevant APIs for data processing and storage.
- Strong communicator capable of conveying complex findings to non-technical audiences efficiently.