Principal Performance Engineer Lead

Job Overview

The Akamai Inference Cloud team is part of Akamai's Cloud Technology Group. We design and operate AI platforms that enable customers to run models with unmatched performance, compliance, and economics. The Model Intelligence & Lifecycle team owns the end-to-end model lifecycle from validation and security scanning through quantization, optimization, and monitoring. We ensure every model meets rigorous standards for quality, safety, and performance.

As an ML Performance Engineer Principal Lead, you will optimize inference performance across the Akamai Inference Cloud. Your focus will be at the intersection of speed and accuracy, applying techniques like quantization, speculative decoding, and hardware‑aware scheduling to maximize throughput and minimize latency. You will collaborate closely with hardware performance engineers to deliver end‑to‑end optimization.

• Applying and evaluating quantization, distillation, and pruning techniques to optimize model performance while preserving accuracy
• Designing hardware‑aware model placement and scheduling strategies to match models with optimal compute resources
• Implementing and tuning speculative decoding, KV‑cache optimization, and batching strategies to improve inference throughput and latency
• Building benchmarking and profiling pipelines to measure model‑layer performance across architectures, hardware, and serving configurations
• Mentoring and guiding engineers on the team through code reviews, design discussions, and technical problem‑solving
• Collaborating with hardware performance engineers to identify and resolve end‑to‑end performance bottlenecks across the inference stack

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

• Your health
• Your finances
• Your family
• Your time at work
• Your time pursuing other endeavors

Akamai is committed to fair and equitable compensation practices. For US‑based candidates only—the base salary for this position ranges from $169,300 to $304,700 per year; a candidate’s salary is determined by various factors including, but not limited to, relevant work experience, skills, certifications, and location. Compensation for candidates outside the US will vary. The compensation package may also include incentive compensation opportunities in the form of annual bonus or incentives, equity awards, and an Employee Stock Purchase Plan (ESPP).

Akamai provides industry‑leading benefits including healthcare, 401(k) savings plan, company holidays, vacation (in the form of PTO), sick time, family‑friendly benefits including parental leave, and an employee assistance program focusing on mental and financial wellness; eligibility requirements apply.

Akamai Technologies is an affirmative action, equal opportunity employer that values the strength that diversity brings to the workplace. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of gender, gender identity, sexual orientation, race/ethnicity, protected veteran status, disability, or other protected group status.