Member of Technical Staff (Software Engineer, Training Infrastructure)
Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
AI @ 6
AWS @ 3
Distributed Systems @ 6
Experimentation @ 3
Go @ 5
Kubernetes @ 3
Machine Learning @ 2
Python @ 5
Reinforcement Learning @ 2
Rust @ 5
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Perplexity is seeking software engineers to join the Training Infrastructure team within AI Research & Systems. The team builds 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 building distributed systems that 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 legible to humans and agents. These tools support use cases ranging from ad hoc experiment retrospectives to closed-loop autoresearch.
A strong candidate will have experience and passion for solving difficult problems at the intersection of AI, distributed systems, and research acceleration.
Responsibilities
- Build distributed systems for orchestrating AI workloads across global-scale compute infrastructure.
- Define user and agent interfaces for designing, managing, and launching experiments.
- Create tools that make experiment results understandable to humans and agents.
- Support experiment retrospectives, closed-loop autoresearch, and accelerated model development.
- Develop and ship reliable, scalable, and performant systems for compute-intensive workloads.
Requirements
- 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 machine learning infrastructure.
- Interest in building systems that accelerate experimentation and model development.
- Bachelor's, master's, or PhD degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
Benefits
Full-time U.S. employees receive a comprehensive benefits program including equity, health, dental, vision, retirement, fitness, commuter, and dependent care accounts, among other benefits. International employees receive a comprehensive benefits program tailored to their region of residence.
The listed USD salary range applies only to U.S.-based positions. Final offer amounts are determined by multiple factors, including experience and expertise, and may vary from the listed range.