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Member of Technical Staff (AI Inference Engineer) @ Perplexity

Perplexity

San Francisco, CAOn-siteFull-time$180k – $280k / year

About this role

Member of Technical Staff (AI Inference Engineer) @ Perplexity - Back to Perplexity’s Job Listings Member of Technical Staff (AI Inference Engineer) Location San Francisco; New York City; Palo Alto Employment Type Full time Department AI Research & Systems Compensation - $220K – $485K • Offers Equity U.S. Benefits Full-time U.S. employees enjoy a comprehensive benefits program including equity, health, dental, vision, retirement, fitness, commuter and dependent care accounts, and more. International Benefits Full-time employees outside the U.S. enjoy a comprehensive benefits program tailored to their region of residence. USD salary ranges apply only to U.S.-based positions. International salaries are set based on the local market. Final offer amounts are determined by multiple factors, including experience and expertise, and may vary from the amounts listed above. We build and run the inference engine behind every Perplexity query and deploy dozens of model architectures at scale with tight latency and cost budgets. Our stack is Rust, Python, CUDA, and CuTe DSL - and we need another engineer to join us. What you will work on Examples of real work the team does: New models support. Support transformer-based retrieval, text-generation, and multimodal models in our inference infrastructure, from weight loading, request scheduling and KV-cache management to support in API Gateway. GPU kernels migration to CuTe DSL. Port our in-house CUDA kernels to NVIDIA's CuTe DSL so they run on GB200 today and are portable to Vera Rubin racks tomorrow. Rust-native serving runtime. Develop our internal Rust-based inference server to solve all Python pains and keep up with rapidly growing traffic. Performance optimisation. Profile and fix bottlenecks from network ingress through continuous batching and GPU kernel interleaving. Reliability and observability. Build dashboards, alerts, and automated remediation so we catch regressions before users d

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