About the role
About us Parallel is a web infrastructure company. Our products are used by leading businesses in sales, marketing, insurance, and coding to build best-in-class AI agents with flexible and powerful programmatic access to the web. We've raised $230 million from Kleiner Perkins, Sequoia, Index Ventures, Spark Capital, Khosla Ventures, First Round, and Terrain to build the web for AIs. We're currently valued at $2 billion and we're forming a world-class team of engineers, designers, marketers, sellers, researchers, and operational experts to achieve our mission. Job: 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. You: 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. Life at Parallel Our team works fully in-person , between our Palo Alto HQ and San Francisco office. We’re a flat, talent-dense organization dedicated to solving technical and creative problems. We seek like-minded individuals who share our passion for applying science, creativity, and consistency to big and complex problems with equally big outcomes. These are our values: Own customer impact: It’s on us to ensure real-world outcomes for our customers. Obsess over craft: Perfect every detail because quality compounds. Accelerate change : Ship fast, adapt faster, and move frontier ideas into production. Create win-wins : Creatively turn trade-offs into upside. Make high-conviction bets : Try and fail. But succeed an unfair amount.