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