Engineering Manager, Perplexity

• 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

Desirable

• 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
• 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
• 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