Interested in this AI Product Manager role at Akamai?
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Do you love building the foundational capabilities that power cloud computing?
Are you looking to bridge the gap between deep technical architecture and product strategy?
Come join our Cloud Technology Group!
This team is revolutionizing Akamai's cloud into a global leader for advanced computing. It involves launching GPU instances, clusters, and AI infrastructure products. These solutions empower enterprises, developers, and researchers to train models, perform inference, and address complex computational challenges. The role requires expertise in GPU technologies, innovative product strategies, and operational precision within a dynamic, results\-driven environment.
Partner with the best
As a Senior Product Manager for GPU Products, you will bring technical expertise, creativity, and focus for impactful outcomes.
As a Senior Product Manager, you will be responsible for:
- Defining product strategy and roadmap for GPU instances, clusters, and services, aligning positioning and requirements to customer and market needs.
- Managing the entire product lifecycle, from planning to end\-of\-life, while creating detailed business analyses and financial models for support.
- Developing and executing go\-to\-market strategies, messaging, and launch plans in partnership with marketing, sales, and solutions engineering.
- Collaborating with ecosystem partners, aligning their roadmap and platform integration with Akamai's product objectives and technical needs.
- Translating AI, HPC, and graphics workload needs into specifications, performance goals, and architectures to enable design success and customer engagement.
- Overseeing GPU infrastructure lifecycle components, utilizing data\-driven decisions on investments from platform evolution to implementing product End\-of\-Life as needed.
- Representing various user objectives while identifying enhancements for proactive monitoring, support processes, escalation workflows, and both planned and unplanned maintenance of Nvidia GPU clusters.
Do what you love
To be successful in this role you will:
- Have 12 years of relevant experience and a Bachelor's degree in Computer Science, Engineering, or its equivalent
- Have maintained a customer\-first focus even when the customers are internal engineers and data scientists, prioritizing automation, usability, and low\-friction integration for GPU workloads.
- Demonstrate expertise in GPU architectures, CUDA ecosystem, and accelerated computing platforms, including resource management and cluster orchestration for AI workloads.
- Have knowledge of cloud networking, GPU interconnects, and infrastructure redundancy in the context of large\-scale GPU deployments.
- Demonstrate proficiency in developing both business and technical models for GPU cloud products, including pricing, profitability, and TCO analysis.
- Maintain familiarity with AI workload patterns, enterprise security requirements, and hardware\-level APIs for GPU instances.
- Build relationships and secure buy\-in from engineering teams regarding new GPU product goals and technical requirements.
About us
At Akamai, we make life better for billions of people, trillions of times a day.
Whether you're streaming live events, scrolling social media, watching your favorite series, or managing your savings, we're the engine behind the scenes. We provide the world's most distributed platform from Cloud to Edge to help the giants of the digital world work faster and stay more secure, making the internet a better experience for everyone.
Our focus is simple:
Cloud and Edge: Running apps closer to users for instant performance.
Security : Neutralizing threats before they ever reach your data.
Content Delivery : Scaling the world's biggest moments without a glitch.
AI : Enabling our customers to build, secure, and scale AI apps on the world's most distributed cloud platform.
At Akamai, we don't just support the internet; we power and protect it, because behind every great digital experience is a massive hidden challenge. And we're the ones who solve it. When millions of people hit play or pay, Akamai ensures it just works.
Benefits at Akamai: We support your health, well\-being, finances, and life beyond work. See our benefits.
FlexBase adapts to your job's needs
Akamai's FlexBase program is yet another way we show our commitment to providing employees with an exceptional workplace experience. It's not about telling employees where to work; it's about supporting employees to do their best work.
We trust our incredible employees to work in ways that suit them best: at home, in an office, or a combination of both.
Connect with us on social and see what life at Akamai is like!
Compensation
Akamai is committed to fair and equitable compensation practices. For US based candidates only \- the base salary for this position ranges from $139,300 \- $250,700/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 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, 401K savings plan, company holidays, vacation (in the form of PTO), sick time, family friendly benefits including parental leave and an employee assistance program including a focus on mental and financial wellness; Eligibility requirements apply.
Salary Context
This $139K-$250K range is above the median for AI Product Manager roles in our dataset (median: $188K across 140 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 3,708 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Akamai, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills in Demand for This Role
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $216,175 based on 270 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($195K) sits 10% below the category median. Disclosed range: $139K to $250K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Akamai AI Hiring
Akamai has 1 open AI role right now. They're hiring across AI Product Manager. Based in US. Compensation range: $250K - $250K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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