Interested in this AI/ML Engineer role at Akamai?
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About This Role
Do you want to shape how AI models are validated, optimized, and deployed at global scale?
Are you passionate about building the systems that ensure AI models perform reliably and responsibly in production
Join the Akamai Inference Cloud Team!
The Akamai Inference Cloud (AIC) team is part of Akamai's Cloud Technology Group. We design and operate AI platforms enabling customers to run models with unmatched performance, compliance, and economics. This team owns the end\-to\-end model lifecyclefrom validation and security scanning through quantization, optimization, and monitoring. We ensure every model meets rigorous standards for quality, safety, and performance.
Partner with the best
As an ML Senior Software Engineer, you will build and operate systems responsible for model validation, quantization, and safety across the AIC. You'll develop pipelines that scan models for vulnerabilities, apply quantization and optimization techniques, and build guardrails that enforce safety and compliance policies. This role requires hands\-on ML experience and deep understanding of modern architectures, inference optimization, and responsible
AI.
\#AIC
As a Machine Learning Senior Software Engineer, you will be responsible for:
- Developing and maintaining model validation and security scanning pipelines that assess models for quality, correctness, and vulnerabilities prior to deployment
- Implementing quantization, pruning, and other optimization techniques to reduce model footprint and improve inference latency across diverse hardware
configurations
- Building guardrail and content safety systems that enforce compliance policies and mitigate risks such as jail breaking and prompt injection
- Designing model routing and prompt management infrastructure that supports intelligent request handling across model variants
- Contributing to model evaluation frameworks that measure accuracy, performance, and safety metrics across the model lifecycle
Do what you love
To be successful in this role you will:
- Have 5 years of relevant experience and a Bachelor's/Master's degree in Computer Science, Machine Learning, or a related field
- Demonstrate hands\-on experience with modern ML frameworks such as PyTorch, TensorFlow, or JAX, including model training, fine\-tuning, and inference optimization
- Show proficiency in model quantization techniques (GPTQ, AWQ, GGUF) and an understanding of how model compression impacts accuracy and latency
- Have experience working with large language models (LLMs), transformer architectures, and multi\-modal AI systems
- Demonstrate familiarity with model safety and responsible AI practices, including content filtering, red\-teaming, or adversarial robustness evaluation
- Show proficiency in Python and experience building production data or ML pipelines
- Have experience with containerized deployments, CI/CD practices, and cloud infrastructure (not required, but suggested)
Work in a way that works for you
FlexBase, Akamai's Global Flexible Working Program, is based on the principles that are helping us create the best workplace in the world. When our colleagues said that flexible working was important to them, we listened. We also know flexible working is important to many of the incredible people considering joining Akamai. FlexBase, gives 95% of employees the choice to work from their home, their office, or both (in the country advertised). This permanent workplace flexibility program is consistent and fair globally, to help us find incredible talent, virtually anywhere. We are happy to discuss working options for this role and encourage you to speak with your recruiter in more detail when you apply.
Learn what makes Akamai a great place to work
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We power and protect life online, by solving the toughest challenges, together.
At Akamai, we're curious, innovative, collaborative and tenacious. We celebrate diversity of thought and we hold an unwavering belief that we can make a meaningful difference. Our teams use their global perspectives to put customers at the forefront of everything they do, so if you are people\-centric, you'll thrive here.
Working for you
At Akamai, we will provide you with opportunities to grow, flourish, and achieve great things. Our benefit options are designed to meet your individual needs for 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
Our benefit plan options are designed to meet your individual needs and budget, both today and in the future.
About us
Akamai powers and protects life online. Leading companies worldwide choose Akamai to build, deliver, and secure their digital experiences helping billions of people live, work, and play every day. With the world's most distributed compute platform from cloud to edge we make it easy for customers to develop and run applications, while we keep experiences closer to users and threats farther away.
Join us
Are you seeking an opportunity to make a real difference in a company with a global reach and exciting services and clients? Come join us and grow with a team of people who will energize and inspire you!
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.
If no date is displayed, applications are being accepted on an ongoing basis until the job is filled.
Compensation
Akamai is committed to fair and equitable compensation practices. For US based candidates only \- the base salary for this position ranges from $121,400 \- $218,600/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 $121K-$218K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 Required
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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($170K) sits 21% below the category median. Disclosed range: $121K to $218K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Akamai AI Hiring
Akamai has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $218K - $218K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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