Inference Engineering Manager at Perplexity AI Ltd

San Francisco, California, United States -

Full Time

Start Date

Immediate

Expiry Date

18 Apr, 26

Salary

0.0

Posted On

18 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, PyTorch, Rust, C++, Kubernetes, ML Systems, Inference Frameworks, LLM Architecture, Inference Optimizations, GPU Characteristics, Distributed Systems, Parallelism Strategies, Technical Communication, Cross-Functional Collaboration

Industry

Software Development

Description

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

How To Apply:

Incase you would like to apply to this job directly from the source, please click here

Responsibilities

Lead and grow a high-performing team of AI inference engineers while developing APIs for AI inference. Architect and scale the inference infrastructure for reliability and efficiency, driving large model inference and improving system observability.