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Principal Performance Engineer Lead

Job in Cambridge, Middlesex County, Massachusetts, 02140, USA
Listing for: Akamai Technologies GmbH
Full Time position
Listed on 2026-06-04
Job specializations:
  • Engineering
    Systems Engineer, Software Engineer
Salary/Wage Range or Industry Benchmark: 169300 - 304700 USD Yearly USD 169300.00 304700.00 YEAR
Job Description & How to Apply Below

Job Summary

The Akamai Inference Cloud team, part of Akamai's Cloud Technology Group, designs and operates 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 to ensure every model meets rigorous standards for quality, safety, and performance.

Responsibilities

As an ML Performance Engineer Principal Lead, you will be responsible for:

  • 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
Qualifications

To be successful in this role you will:

  • Have 12+ years of relevant experience with a Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field
  • 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, Tensor

    RT‑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

Our benefit plan options are designed to meet your individual needs and budget, both today and in the future. We provide benefits surrounding all aspects of your life:

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

Akamai is committed to fair and equitable compensation practices. For U.S. based candidates only, the base salary for this position ranges from $169,300 — $304,700/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 U.S. 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 such as parental leave and an employee assistance program focusing on mental and financial wellness; eligibility requirements apply.

EEO Statement

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.

Job Info
  • Job Identification 2677
  • Posting Date 04/07/2026, 01:45 PM
  • Locations 145 Broadway, Cambridge, MA, 02142, US (Remote)
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