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Perplexity is hiring software engineers for the Training Infrastructure team, responsible for building large-scale reinforcement learning (RL) and agent evaluation platforms that enable the company to train state-of-the-art models and optimize agent systems.
In this role, you will work as a full-stack engineer with broad ownership across multiple domains. Your responsibilities include:
- Building distributed systems that orchestrate AI workloads across global-scale compute infrastructure
- Designing novel user and agent interfaces for designing, managing, and launching experiments
- Creating tools that make experiment results legible to both humans and AI agents, enabling ad-hoc retrospectives and closed-loop autoresearch capabilities
- Solving difficult problems at the intersection of AI, distributed systems, and research acceleration
You'll work in an environment that values craftsmanship, ownership, and entrepreneurship. The team emphasizes high-quality product development, individual initiative in identifying and solving problems, and collaboration among talented peers.
QUALIFICATIONS:
- 2+ years of experience developing and shipping distributed systems for compute-intensive workloads, with demonstrated focus on reliability, scalability, and performance
- Proficiency in a general-purpose backend language (Python, Rust, C++, or Go)
- Experience with cloud infrastructure and container orchestration (AWS, Kubernetes)
- Familiarity with reinforcement learning (RL), agent evaluation, or ML infrastructure
- Interest in building systems that accelerate experimentation and model development
- BS, MS, or PhD in Computer Science, Engineering, or related field, or equivalent practical experience
About Perplexity AI
AI / Data / Infrastructure — AI answer engine and search product for consumers and enterprises.
