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Parallel is seeking a professional who will own the training pipeline behind models that support both the search stack and agents. Responsibilities include building pathways from product usage to high-quality training data, rigorously fine-tuning models, and shipping them for high-traffic usage.
The ideal candidate should have a strong grasp of modern training models, especially in the realms of transformer architectures and data curation, ensuring applied research benefits millions of users.
You will own the training pipeline behind the models that power both Parallel’s search stack and Parallel’s agents. On the search side, that means the rankers, classifiers, and query models that surface the right information. On the agent side, that means the models that help our agents plan, reason, and execute high‑value tasks over web data. You will build the path from real product usage to high‑quality training data, fine‑tune and evaluate these models rigorously, and ship them safely to traffic used by millions.
Have deep intuition on modern models and training, including transformer fine‑tuning, data curation, and the craft of label quality. Think rigorously about how ranking, retrieval, and agent behavior inform one another, and how to train models that serve all three. You care about your research being applied to product and systems that millions use.