2+ years of experience in developing distributed systems for compute-intensive workloads, emphasizing reliability and scalability.
Proficiency in backend languages like Python, Rust, C++, or Go, along with experience in cloud infrastructure and container orchestration (AWS, Kubernetes).
Familiarity with reinforcement learning (RL) and ML infrastructure, with a focus on enhancing experimentation and model development.
Educational background: BS, MS, or PhD in Computer Science, Engineering, or a related field, or equivalent practical experience.
Responsibilities
Build distributed systems to manage AI workloads across global-scale compute infrastructure.
Define user and agent interfaces for designing, managing, and launching experiments.
Create tools to make experiment results understandable for both humans and agents.
Facilitate ad-hoc experiment retrospectives and closed-loop autoresearch.
Collaborate with cross-functional teams to optimize model training processes.
Benefits
Opportunity to work on cutting-edge AI technologies and large-scale systems.
Collaborative environment with a focus on innovation and problem-solving.
Access to professional development resources and training.
Flexible work arrangements to support work-life balance.
Full Job Description
Perplexity is seeking software engineers to join our Training Infrastructure team. We're responsible for the large-scale RL and agent evaluation platforms that enable Perplexity to train state-of-the-art models and optimize agent harnesses.
We are full-stack engineers with a broad remit. If you join us, you'll build the distributed systems that orchestrate AI workloads across global-scale compute infrastructure. You'll also define novel user and agent interfaces for designing, managing, and launching experiments. You'll create tools that make experiment results legible to humans and agents alike, enabling everything from ad-hoc experiment retrospectives to closed-loop autoresearch. And much more!
A strong candidate will possess both experience and passion for solving difficult problems at the intersection of AI, distributed systems, and research acceleration.
Qualifications
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, and experience with cloud infrastructure and container orchestration, such as AWS and Kubernetes.
Familiarity with reinforcement learning (RL), 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.
