Machine Learning Engineer

Summary

Join Federato, a well-funded insurtech company, and become a Machine Learning Engineer. You will play a crucial role in deploying and optimizing our AI/ML-driven platform, focusing on customer-specific use cases. Responsibilities include deploying LLM-powered pipelines, building and maintaining a prompt repository, leading technical PoC projects, and collaborating with engineering and product teams. The ideal candidate possesses proven experience in machine learning, particularly with NLP models and LLMs, and is proficient in Python and relevant libraries. Federato offers a competitive salary ($120,000 - $150,000 annually), stock options, benefits, and a dynamic work environment.

Requirements

• Proven experience as a Machine Learning Engineer or similar role (at least 6 months of relevant internship experience in a similar role), with a strong focus on building and benchmarking models
• Practical experience through coursework, internships, or personal projects in applying machine learning techniques, ideally with some exposure to NLP models, prompt engineering, and working with LLMs (e.g., via frameworks like Hugging Face, OpenAI)
• Familiarity with version control (Git), CI/CD pipelines, and deploying ML models or services locally or in cloud environments. Knowledge of GitHub for collaboration and maintaining codebases is essential
• Proficiency in Python for building and testing ML models, with experience using popular ML and data libraries like TensorFlow, PyTorch, Scikit-learn, Pandas, and NumPy

Responsibilities

• Quickly deploy and test the current LLM-powered pipeline locally for specific customer use cases, making necessary adjustments to ensure alignment with customer requirements, and documenting findings clearly
• Take ownership of deploying product improvements based on customer-specific requests, pushing code and configuration changes to GitHub, and collaborating closely with product and engineering teams for smooth integration
• Build and maintain a repository of prompts for various scenarios that would be leveraged for any prospective customer proof of concept, iterating on these to optimize performance and ensure they are production-ready when needed
• Lead technical PoC projects with prospective customers, showcasing the capabilities of the ML models, addressing their specific needs, and gathering feedback to inform future improvements
• Collaborate with the team to ensure continuous monitoring of deployed models, focusing on improving accuracy and performance within a structured ML framework while learning best practices for pipeline maintenance

Preferred Qualifications

Enthusiasm for customer-facing work, including the ability to understand specific use cases and adjust ML models accordingly. They should be proactive in learning deployment best practices and taking responsibility for PoCs and prompt engineering tasks

Benefits

• $120,000 - $150,000 a year
• Stock options
• Benefits
• Additional perks