Member of Technical Staff (Software Engineer, Training Infrastructure)

Perplexity • San Francisco, CA

1d • $220,000 - $405,000

About The Position

Perplexity is seeking software engineers to join its Training Infrastructure team, which is responsible for large-scale reinforcement learning and agent evaluation platforms that enable Perplexity to train state-of-the-art models and optimize agent harnesses. The role involves full-stack engineering across a broad remit, including building distributed systems to orchestrate AI workloads across global-scale compute infrastructure; defining user and agent interfaces for designing, managing, and launching experiments; and creating tools that make experiment results accessible to humans and agents. These tools support activities ranging from ad-hoc experiment retrospectives to closed-loop autoresearch. The role suits someone with experience and interest in solving challenging problems at the intersection of AI, distributed systems, and research acceleration.

Requirements

• At least 2 years of experience developing and shipping distributed systems for compute-intensive workloads, with a focus on reliability, scalability, and performance.
• Proficiency in a general-purpose backend language such as Python, Rust, C++, or Go.
• Experience with cloud infrastructure and container orchestration, such as AWS and Kubernetes.
• Familiarity with reinforcement learning, agent evaluation, or ML infrastructure, and an interest in building systems that accelerate experimentation and model development.
• BS, MS, or PhD in Computer Science, Engineering, or a related field, or equivalent practical experience.

Responsibilities

• Build distributed systems that orchestrate AI workloads across global-scale compute infrastructure.
• Define user and agent interfaces for designing, managing, and launching experiments.
• Create tools that make experiment results legible to humans and agents, supporting experiment retrospectives and closed-loop autoresearch.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level