Member of Technical Staff (Backend Software Engineer, API Platform)
Perplexity · San Francisco
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About the Role
Perplexity is redefining how people search, reason, and interact with information. Our API team sits at the core of this vision, designing and operating the high-performance interfaces that expose Perplexity's intelligence to developers, enterprises, and products worldwide. This team owns the architecture, reliability, and security of the APIs that power everything from real-time LLM queries to enterprise-scale integrations.
As a member of our API Platform engineering team, you will shape the technical foundation of Perplexity's external platform. You'll architect distributed systems, optimize performance at scale, and set engineering standards that ensure our APIs are fast, resilient, and a delight to build on.
Tech Stack: Python | Go | PostgreSQL | DynamoDB | Redis | FastAPI | React | Bazel | GitHub | AWS
Key Responsibilities
Design, build, and operate mission-critical APIs in Python, Go, or Rust optimized for low latency, high throughput, and strong consistency.
Lead major technical initiatives, from new platform components to multi-team system redesigns.
Improve system reliability and security by implementing rate limiting, authentication, observability (metrics, tracing, logging), and automated testing.
Collaborate across teams to determine when to extend existing systems versus build new, specialized services.
Translate customer needs into scalable platform capabilities and generalized API features.
Uphold engineering excellence through design reviews, code quality, and internal documentation.
Qualifications
5+ years of backend engineering experience with Python, Go, or Rust.
Strong understanding of high-traffic API design: versioning, schema evolution, idempotency, auth patterns, and performance tuning.
Hands-on experience running AWS services, plus fluency with Kubernetes, CI/CD, and modern DevOps practices.
Experience with LLM or natural-language APIs (latency tuning, streaming, model orchestration) is a strong plus.
Strong customer empathy and product sense, ensuring the APIs you build are ergonomic, well-documented, and easy to adopt.
In information theory, perplexity is a measure of uncertainty for a discrete probability distribution. The perplexity of a fair coin toss is 2, and that of a fair die roll is 6; and generally, for a probability distribution with exactly N outcomes each having a probability of exactly 1 / N, the perplexity is simply N. But perplexity can also be applied to unfair dice, and to other non-uniform probability distributions. It can be defined as the exponentiation of the information entropy. The larger the perplexity, the less likely it is that an observer can guess the value which will be drawn from the distribution.
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