Interested in this AI/ML Engineer role at Akamai?
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Are you interested in driving customer and developer experience for next generation cloud services?
Do you enjoy creating products that customers love?
Join our Cloud Technology Group!
As part of Akamai's Cloud Technology Group (CTG), our mission is to empower customers to build powerful, distributed applications, through a simple, scalable, AI and cloud platform. We provide the infrastructure and services that enable developers and enterprises to build next generation edge native applications.
Partner with the best
You will lead the product team managing Akamai's AI infrastructure and inference initiatives, from GPU clusters to scalable model deployment. Capitalizing on substantial company investments, defining product strategy, refining offerings, and ensuring differentiation as the AI industry evolves.
As a Senior Director of Product Management AI, you will be responsible for:
- Driving long-term product vision and strategy for AI infrastructure, leveraging insights to identify differentiation opportunities, shape positioning, and defining investment priorities.
- Owning business outcomes across GPU and cloud inferencing portfolio, including revenue growth, adoption, and customer satisfaction.
- Collaborating with go-to-market leadership on pricing, packaging, and segment strategy, and representing Akamai externally with customers, partners, and analysts.
- Leading cross-functional execution across product, engineering, and SRE to advance our AI platform, delivering GPU infrastructure and serverless inference innovations from concept through scale
- Building and leading a high-performing team of product leaders, driving accountability, execution discipline, and a culture of customer obsession.
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 or related field.
- Demonstrate expertise in AI infrastructure, including GPU systems, model deployment, serverless inference, and technologies supporting scalable AI workloads.
- Have direct product experience building and launching commercial AI infrastructure or inference platforms, from product definition through launch, adoption, and monetization.
- Build the foundational platforms and services enabling developers to deploy and manage AI workloads, instead of relying on third-party solutions.
- Bring a successful track record of building and scaling product management organizations and establishing effective operating rhythms.
- Have executive presence and excellent communication skills, representing the company effectively with customers, partners, and analysts.
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 $190,700 - $343,300/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 $190K-$343K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Akamai, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($267K) sits 26% above the category median. Disclosed range: $190K to $343K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Akamai AI Hiring
Akamai has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $343K - $343K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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