Data Product Engineer

Effective AI•San Francisco, CA

6d•$210,000 - $270,000•Onsite

About The Position

Effective AI is building a software factory for complex industries, aiming to create a system of record for how businesses operate. The company focuses on turning operating knowledge into governed systems that humans and agents can trust, extend, and act on, starting with the insurance industry. They are developing reusable industry models and a language called RSL (Rater Specification Language) to represent insurance logic in a typed, testable, and executable format. Customers can inherit a governed industry model, specialize it for their needs, and maintain its accuracy as the shared model improves. The company has secured $10 million in seed funding from Lightspeed Ventures & Valor Equity Partners.

Requirements

Strong computer science foundation.
Track record of shipping systems that were challenging due to scale, ambiguity, messy data, product constraints, reliability, or organizational complexity.
Ability to operate without a playbook.
Capacity to turn unclear problems into crisp models.
Skill in making appropriate simplifying assumptions.
Ability to build initial versions that can evolve and scale incrementally.
Natural inclination to use agents for leverage in research, extraction, validation, testing, and operational work.
Relentless optimization of the agent harness (tools, context, evals, workflows).
Willingness to explore ambitious technical directions.
Ability to conduct fast experiments with clear learning goals.
Engineering taste to know when to commit or change course.

Responsibilities

Design and build the layer through which Effective perceives the outside world.
Extract information from sources like new filings, regulations, bureau circulars, and external data feeds.
Transform raw source data into trustworthy signals for business operations.
Own the entire process from raw source data to agents that read and reason over it, to product surfaces where it provides value.
Ship real-time search capabilities over large datasets.
Develop verification-first AI systems with source-level audit trails.
Build and maintain a production multi-agent runtime.
Improve upstream performance for faster code agent boot times.