About the job
Founding Machine Learning Engineer
Location: San Francisco, CA Work Model: In-office 5 days a week
About Us
At Effective AI, we are pioneering the future of work. Our vision is to push the boundaries of AI beyond mere repetitive tasks, focusing instead on intricate knowledge work that requires expertise and multi-faceted reasoning. We are developing advanced AI Teammates that are designed to navigate complex workflows and collaborate seamlessly with human professionals. Our initial focus is on the trillion-dollar U.S. Property & Casualty insurance sector, a domain rich with complexity and data, making it an ideal arena for our innovations.
We proudly secured $10 million in seed funding from prominent investors including Lightspeed Ventures and Valor Equity Partners.
Our committed team is based in San Francisco and thrives on in-person collaboration to tackle these significant challenges.
Your Role
As a Founding Machine Learning Engineer, you will be an integral member of our founding team, responsible for architecting, training, and deploying the agent loops that power our AI Teammates from inception. You will address some of the most pressing challenges in agentic AI and natural language processing, developing AI solutions adept at performing essential insurance functions such as underwriting and claims processing.
Your responsibilities will include:
- Architecting and Developing Core ML Pipelines: Design, train, and fine-tune cutting-edge language models (including reinforcement learning agents) to facilitate long-term task accomplishment and complex decision-making.
- Implementing Nuanced Reasoning: Integrate machine learning techniques that empower agents to make informed decisions based on ambiguous or incomplete data, akin to human expert reasoning and generalization.
- Building Intelligent, Tool-Using Agents: Engineer the ML systems that enable our agents to dynamically select and utilize a broad array of external tools—including APIs, databases, web searches, and Excel-based pricing algorithms—to gather necessary information and execute actions.
- Designing and Implementing Robust Evaluation Frameworks: Create and employ comprehensive evaluation metrics and systems to rigorously assess and benchmark agent performance, identify areas for enhancement, and guarantee reliability and safety in real-world insurance processes.
- Enabling Continuous Adaptation and Learning: Develop resilient ML pipelines and feedback loops that facilitate ongoing learning and adaptation.