Inference Engineering Manager

San Francisco

### About the Role We are looking for an Inference Engineering Manager to lead our AI Inference team. This is a unique opportunity to build and scale the infrastructure that powers Perplexity's products and APIs, serving millions of users with state-of-the-art AI capabilities. You will own the technical direction and execution of our inference systems while building and leading a world-class team of inference engineers. Our current stack includes Python, PyTorch, Rust, C++, and Kubernetes. You will help architect and scale the large-scale deployment of machine learning models behind Perplexity's Comet, Sonar, Search, Deep Research products. ### Why Perplexity? * Build SOTA systems that are the fastest in the industry with cutting-edge technology * High-impact work on a smaller team with significant ownership and autonomy * Opportunity to build 0-to-1 infrastructure from scratch rather than maintaining legacy systems * Work on the full spectrum: reducing cost, scaling traffic, and pushing the boundaries of inference * Direct influence on technical roadmap and team culture at a rapidly growing company ### Responsibilities * Lead and grow a high-performing team of AI inference engineers * Develop APIs for AI inference used by both internal and external customers * Architect and scale our inference infrastructure for reliability and efficiency * Benchmark and eliminate bottlenecks throughout our inference stack * Drive large sparse/MoE model inference at rack scale, including sharding strategies for massive models * Push the frontier with building inference systems to support sparse attention, disaggregated pre-fill/decoding serving, etc. * Improve the reliability and observability of our systems and lead incident response * Own technical decisions around batching, throughput, latency, and GPU utilization * Partner with ML research teams on model optimization and deployment * Recruit, mentor, and develop engineering talent * Establish team processes, engineering standards, and operational excellence ### Qualifications * 5+ years of engineering experience with 2+ years in a technical leadership or management role * Deep experience with ML systems and inference frameworks (PyTorch, TensorFlow, ONNX, TensorRT, vLLM) * Strong understanding of LLM architecture: Multi-Head Attention, Multi/Grouped-Query Attention, and common layers * Experience with inference optimizations: batching, quantization, kernel fusion, FlashAttention * Familiarity with GPU characteristics, roofline models, and performance analysis * Experience deploying reliable, distributed, real-time systems at scale * Track record of building and leading high-performing engineering teams * Experience with parallelism strategies: tensor parallelism, pipeline parallelism, expert parallelism * Strong technical communication and cross-functional collaboration skills ### Nice to Have * Experience with CUDA, Triton, or custom kernel development * Background in training infrastructure and RL workloads * Experience with Kubernetes and container orchestration at scale * Published work or contributions to inference optimization research