Jobs at CHAI AI

Build and scale backend services connecting 10 million users with an AI model fleet. Responsibilities include routing, conversation state management, and queuing while maintaining high volume, low latency, and low error rates. The role offers end-to-end ownership in a fast-moving, in-person Palo Alto engineering team.

Build and ship high-impact frontend and backend features for CHAI AI’s social AI platform, serving millions of users. The role offers broad ownership across the stack, requires defining problems and solutions in a fast-moving, flat engineering team, and involves rapid product iteration with measurable outcomes. Engineers work in person at the Palo Alto office and complete a fast interview process including a take-home assessment and onsite interview.

Own and maintain infrastructure supporting a high-scale social AI platform, including deployment systems, monitoring, GPU cluster management, and CI/CD. The role requires strong ownership, comfort with ambiguity, and the ability to define infrastructure problems and solutions independently. The engineer will work in person from the company’s Palo Alto office and help ensure reliable, low-downtime operations for a rapidly shipping engineering team.

Own the system that enables AI characters to remember users across hundreds of conversations, including memory schema design, information extraction, and compression. Develop and evaluate improvements based on user retention and recall accuracy. Work with a small, fast-moving engineering team in person at the Palo Alto office, taking ownership of ambiguous problems and defining effective solutions.

Own the research agenda for AI model training and evaluation, including developing new training objectives and evaluation metrics. Connect research outcomes directly to revenue and product performance in a fast-moving social AI startup. The role requires high ownership, comfort with ambiguity, strong motivation, software engineering experience, and in-person work at the Palo Alto office.

Own the post-training pipeline for AI roleplay models, transforming raw data into improved model behavior through supervised fine-tuning, reinforcement learning, reranking, data curation, and evaluation. The role involves closing the loop from evaluation signals to training runs, defining problems independently, and working onsite with a small, fast-moving engineering team.

Own AI model-serving infrastructure for a platform with 10 million users. Optimize model fit within fixed GPU budgets, improve serving speed and latency, manage cost per token, determine when to optimize or rebuild systems, and plan capacity expansion. The role requires high ownership, comfort with ambiguity, and in-person work at the Palo Alto office.