About the job
Join Our Team
At Parallel, we are at the forefront of web infrastructure innovation, empowering businesses in various sectors, sales, marketing, insurance, and technology, to develop sophisticated AI agents equipped with robust programmatic access to the internet.
Having secured $130 million in funding from prestigious investors such as Kleiner Perkins, Index Ventures, Spark Capital, Khosla Ventures, First Round, and Terrain, we are building a premier team of engineers, designers, marketers, sales professionals, researchers, and operational specialists to fulfill our ambitious vision.
Your Profile
We are looking for a researcher who embodies an engineering mindset, or an engineer who approaches problems with curiosity typical of researchers. You may have experience with information retrieval systems, embedding models, or neural ranking at scale, or possess a deep interest in the challenges of training models to comprehend and navigate billions of web pages. You will excel in the intersection of theory and practical application, devising elegant solutions that perform efficiently on real-world infrastructure. You'll be equally comfortable reading the latest papers from SIGIR and RecSys as you are troubleshooting distributed training pipelines.
Position Overview
In this role, you will design and train models that drive Parallel's APIs, the intelligent framework that enables AI agents to extract precise information from the open web. This involves addressing complex research challenges that most labs only encounter at scale: How can we create embedding models that accurately represent semantic intent across various query types? How do we achieve a balance between model expressiveness and sub-second retrieval times? How can we ensure our index remains up-to-date with the constantly evolving web, without the need for complete rebuilds?
Unlike conventional search engines tailored for human queries, you will be developing solutions for AI agents that generate intricate, multi-hop queries, requiring structured, programmatic responses. This is information retrieval redefined for the era of large language models, merging traditional information retrieval methods with cutting-edge deep learning, applied at a scale that necessitates innovative solutions.
Working Environment
Our team collaborates fully in-person at our headquarters in Palo Alto and our San Francisco office. We pride ourselves on being a flat, talent-rich organization committed to tackling both technical and creative challenges.
We are eager to welcome individuals who share our enthusiasm for leveraging science, creativity, and consistency to address large, complex problems with significant impacts. Here are our core values:
- Customer Impact Ownership: We take responsibility for delivering tangible results for our clients.
