Principal Performance Engineer – Lead

🕒 April 7

🍂 Massachusetts – Remote

🍂 Massachusetts – Remote

💵 $169.3k - $304.7k / year

⏰ Full Time

🟠 Senior

👷🏻‍♀️ Engineer

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Akamai Technologies

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

🔒 Cybersecurity

💰 Post-IPO Equity on 2001-07

Cloud Computing • Cybersecurity • Content Delivery

Akamai Technologies is a leading cloud services provider that specializes in delivering security, cloud computing, and content delivery solutions. It offers a range of services such as API security, DDoS protection, and performance optimization for web applications, ensuring secure and reliable user experiences. With a robust global infrastructure, Akamai empowers businesses to streamline their digital presence while safeguarding against various cyber threats and enhancing application performance.

📋 Description

• Optimize inference performance across the Akamai Inference Cloud • Collaborate closely with hardware performance engineers to deliver end-to-end optimization • Apply and evaluate quantization, distillation, and pruning techniques to optimize model performance while preserving accuracy • Design hardware-aware model placement and scheduling strategies to match models with optimal compute resources • Implement and tune speculative decoding, KV-cache optimization, and batching strategies to improve inference throughput and latency • Build benchmarking and profiling pipelines to measure model-layer performance across architectures, hardware, and serving configurations • Mentor and guide engineers on the team through code reviews, design discussions, and technical problem-solving • Collaborate with hardware performance engineers to identify and resolve end-to-end performance bottlenecks across the inference stack

🎯 Requirements

• 12+ years of relevant experience with a Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field • Possess hands-on experience optimizing LLM inference performance (quantization, speculative decoding, model compression, etc.) • Have a solid understanding of transformer architectures and how design choices impact latency, throughput, and accuracy • Possess experience with inference serving frameworks such as vLLM, TensorRT-LLM, Triton, or similar systems • Be proficient in Python and C++ with experience profiling and optimizing compute-intensive workloads • Have familiarity with hardware-aware optimization, including GPU/accelerator scheduling and memory management trade-offs

🏖️ Benefits

• healthcare • 401K savings plan • company holidays • vacation (in the form of PTO) • sick time • family friendly benefits including parental leave • employee assistance program including a focus on mental and financial wellness