Engineering Manager (AI Inference) at Perplexity

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