Akamai Technologies

1 week ago

Senior Product Manager (AI Infrastructure & GPU)

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 14 hours agoStatus: Live

Job Summary

Own the strategy and roadmap for GPU instances, clusters, and AI infrastructure products, bridging deep technical architecture with product direction. Lead the full product lifecycle, create business analyses and financial models, and drive go-to-market messaging with marketing, sales, and solutions engineering. Partner with ecosystem teams, translate AI/HPC workloads into performance and architecture goals, and guide GPU infrastructure lifecycle, monitoring, and maintenance for Nvidia GPU clusters.

Location: Cambridge

Workplace: Remote

Employment Type: Full time

Job Function: Product Management

Seniority: Manager level

Key Responsibilities

•Define GPU product strategy and roadmap for GPU instances, clusters, and services, aligning positioning and requirements to customer and market needs.
•Manage the full product lifecycle from planning through end-of-life, including business analyses and financial models.
•Develop and execute go-to-market strategies, messaging, and launch plans with marketing, sales, and solutions engineering.
•Collaborate with ecosystem partners to align roadmaps and platform integration with Akamai’s product objectives and technical needs.
•Translate AI/HPC/graphics workload needs into specifications, performance goals, and architectures; oversee GPU infrastructure lifecycle, proactive monitoring, and maintenance for Nvidia GPU clusters.

Pay and Benefits

Salary: USD 139,300 - 250,700 annually

Equity and Bonus:Equity

Perks:Health Insurance401kPaid LeaveSick TimeParental Leave

Key Requirements

•12 years of relevant experience and a Bachelor's degree in Computer Science, Engineering, or equivalent.
•Customer-first mindset for internal engineering and data science teams, with focus on automation, usability, and low-friction GPU workload integration.
•Expertise in GPU architectures, the CUDA ecosystem, and accelerated computing platforms, including resource management and cluster orchestration for AI workloads.
•Knowledge of cloud networking, GPU interconnects, and infrastructure redundancy for large-scale GPU deployments.
•Ability to develop business and technical models for GPU cloud products, including pricing, profitability, and TCO analysis.

Experience:12+ yearsCloud computingAIHPCGPU infrastructure

Education:Bachelor's in Computer Science, Engineering

Skills:Customer-first focusAutomationRelationship buildingData-driven decision-makingCross-functional collaboration

Tech Stack:GPUNvidia GPU clustersCUDAAIHPCCluster orchestrationCloud networkingGPU interconnectsInfrastructure redundancy

Company Brief

Akamai Technologies

Provides a global content delivery network (CDN) and cloud services to improve web and application performance, security, and delivery for enterprises, media companies, and cloud providers.

Industry: Cloud Computing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Cambridge, United States

Founded: 1998

Website LinkedIn

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Akamai Technologies

1 week ago

Senior Product Manager (AI Infrastructure & GPU)

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 14 hours agoStatus: Live

Job Summary

Location: Cambridge

Workplace: Remote

Employment Type: Full time

Job Function: Product Management

Seniority: Manager level

Key Responsibilities

•Define GPU product strategy and roadmap for GPU instances, clusters, and services, aligning positioning and requirements to customer and market needs.
•Manage the full product lifecycle from planning through end-of-life, including business analyses and financial models.
•Develop and execute go-to-market strategies, messaging, and launch plans with marketing, sales, and solutions engineering.
•Collaborate with ecosystem partners to align roadmaps and platform integration with Akamai’s product objectives and technical needs.
•Translate AI/HPC/graphics workload needs into specifications, performance goals, and architectures; oversee GPU infrastructure lifecycle, proactive monitoring, and maintenance for Nvidia GPU clusters.

Pay and Benefits

Salary: USD 139,300 - 250,700 annually

Equity and Bonus:Equity

Perks:Health Insurance401kPaid LeaveSick TimeParental Leave

Key Requirements

•12 years of relevant experience and a Bachelor's degree in Computer Science, Engineering, or equivalent.
•Customer-first mindset for internal engineering and data science teams, with focus on automation, usability, and low-friction GPU workload integration.
•Expertise in GPU architectures, the CUDA ecosystem, and accelerated computing platforms, including resource management and cluster orchestration for AI workloads.
•Knowledge of cloud networking, GPU interconnects, and infrastructure redundancy for large-scale GPU deployments.
•Ability to develop business and technical models for GPU cloud products, including pricing, profitability, and TCO analysis.

Experience:12+ yearsCloud computingAIHPCGPU infrastructure

Education:Bachelor's in Computer Science, Engineering

Skills:Customer-first focusAutomationRelationship buildingData-driven decision-makingCross-functional collaboration

Tech Stack:GPUNvidia GPU clustersCUDAAIHPCCluster orchestrationCloud networkingGPU interconnectsInfrastructure redundancy

Company Brief

Akamai Technologies

Provides a global content delivery network (CDN) and cloud services to improve web and application performance, security, and delivery for enterprises, media companies, and cloud providers.

Industry: Cloud Computing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Cambridge, United States

Founded: 1998

Website LinkedIn