Principal Performance Engineer, Lead

Job not on LinkedIn

🕒 April 7

🍂 Massachusetts – Remote

🍂 Massachusetts – Remote

💵 $169.3k - $304.7k / year

⏰ Full Time

🟠 Senior

🧑‍💻 Full-stack Engineer

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Akamai Technologies

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

🔒 Cybersecurity

🏢 Enterprise

📱 Media

Cybersecurity • Enterprise • Media

Akamai Technologies is a global edge platform and cloud services company that delivers content delivery, edge computing, and security solutions. The company operates one of the world’s largest distributed networks to accelerate and protect web, media, and application traffic, offering products for content delivery, DDoS protection, API and app security, bot management, edge compute (serverless/edge functions), and AI inference at the edge. Akamai also provides enterprise-focused security services (zero trust, identity and access management, secure internet access) and cloud/AI infrastructure tools, and has recently expanded capabilities through acquisitions (for example LayerX) to add browser-based AI usage control.

📋 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

• Health insurance • 401K savings plan • Company holidays • Vacation (in the form of PTO) • Sick time • Family friendly benefits including parental leave • Employee assistance program with focus on mental and financial wellness