Forward Deployed Engineer, Research

Talentfinest•San Francisco, CA
•Onsite

About The Position

The company works on the data and infrastructure problems behind frontier model training and deployed AI agents. It partners directly with labs on post-training research and infrastructure, and deploys models and agents for large enterprises and government customers. Less than four months into operation, the company is already profitable with over seven figures in realized revenue and additional contracted revenue. The small team includes engineers and early employees from leading enterprise software, AI, and semiconductor companies, supported by early institutional and angel investors including senior researchers from leading AI labs. As a Forward Deployed Engineer, you sit inside a customer’s team and own the work of making their agents and models better. You are not shipping a generic product. You are embedded with a specific customer, working from real usage, and accountable for that relationship’s outcomes end to end.

Requirements

  • Agent systems or FDE experience: At least one year of significant experience building AI agent systems used by real people, or technical forward-deployed experience with direct coding ownership.
  • Strong software fundamentals in any stack, supported by a computer science background and real software engineering experience.
  • Ability to work effectively with highly technical customers, especially researchers and engineers rather than only business buyers.
  • Comfort owning a customer relationship directly and being accountable for outcomes.

Responsibilities

  • Turn live usage into training data: Build pipelines that pull signal from production logs, including which answers users keep, override, or later correct, and turn that signal into training data without a human labeling step in the middle.
  • Test models that are already live: Build scoring that is inexpensive enough to run on customer traffic, tuned to what each customer considers good, and sensitive enough to catch a broken workflow before overall metrics move.
  • Trace what data caused what: Build systems that connect changes in model behavior to the data that drove those changes, reducing guesswork in the training loop.
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