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About This Role
DESCRIPTION
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Amazon Entertainment is seeking an experienced HR professional to architect our executive talent strategy for our Entertainment business – where creative-meets-technology-meets business across music, games, podcasts, and other mediums of entertainment.
This is an opportunity to influence how one of the world's most innovative companies develops its talent for the future. You’ll work directly with our senior Entertainment executives as we go all in with AI and shape the workforce transformation that’s to come.
This is not a talent acquisition or recruiting role. Those with experience as an HR Business Partner to senior executives, with strong business acumen, executive coaching expertise, organization design, excellent written and verbal communication skills, and robust project management capabilities often have the right translatable skills to be successful in this role. This person will also be comfortable with presenting regularly with executive and C-suite leaders, with the confidence to facilitate debate with leaders at this level.
The right person for this role needs to thrive in dealing with a high level of ambiguity and feeling comfortable operating in a space with little direction for solving complex talent and organizational business problems. While we align with existing Amazon timelines on high level core talent activities (e.g., evaluation and assessment, promotions, annual talent reviews), this leader will help evolve our talent processes to fit Entertainment talent needs using creativity to design, build, and manage process execution for our Entertainment executives.
About the Business:
The role is part of the Amazon People eXperience and Technology (PXT) organization – we build a workplace for Amazonians to invent and deliver on behalf of customers. PXT for Amazon Entertainment supports a portfolio of entertainment businesses that span film, TV, live sports, music, podcasts, games, audiobooks, livestreaming, and other media content. The Entertainment PXT team focuses on the inputs that attract, grow, and retain creative, technical, and business talent at scale. We customize for unique Entertainment business and industry needs where necessary and scale where possible and applicable. Our Entertainment employees deliver world-class products and services for playing, listening, watching, and streaming in an ever-immersive entertainment environment.
Key job responsibilities
This Executive Talent Lead’s responsibilities include building and continuously evolving Entertainment’s talent reviews and cyclical processes in close partnership with HR Business Partners, business leaders, analytics teams, and central Amazon talent leads. You will be a talent advisor to both business and HR leaders, and ensure we are constantly improving and scaling the services we provide.
The ideal candidate must be a strong systems thinker with strong business acumen, ownership, grit, and drive for results. A successful candidate will have a demonstrated track record of:
- Talent management execution. Design the overall talent review strategy focused on business-critical talent issues, which includes succession planning, talent development, long term talent planning, organizational reviews (and integrate with diversity and inclusion). Enhance and revisit executive level talent activities (Talent reviews, Career development, organizational capacity planning).
- Embracing ambiguity, and building structure where processes don’t yet exist. Understand all the connections and integration points through the entire talent management lifecycle, and using your program management expertise to lead the organization through these activities.
- Communicating with multiple stakeholders with concise, clear written and verbal methods. We are a document-heavy culture, which means we write, a lot, to share our ideas, our proposals, and everything else.
- Curiosity about your business. Learning your clients’ business, understanding their priorities, and recommending the best talent activities to help them meet their business customer needs.
- Building relationships and earning trust as a talent expert. Partnering with central team talent leaders, HR business partners, and business leaders and executives.
- Innovating with both simple yet creative solutions to address talent challenges.
- Operating with high judgment. There will be a constant flow of work, both tactical and strategic. Determine what gets done first and why, while managing a plan for what to do with everything else.
- Using research and data to inform your recommendations. We seek to be the most scientific HR organization in the world. We form hypotheses about the best talent management techniques and then set out to prove or disprove them with experiments and careful data collection. We pilot new ideas, measure and seek feedback, iterate and continuously improve.
- Resourcefulness. If you don’t know it, that’s ok. But you should know where to go for the answer or how to find out.
A day in the life
This leader will quickly earn trust with HR peers, HR senior leaders, and business line leaders, and get deep in understanding specific areas of the Entertainment business. You will build cross-Amazon partnerships with Talent Management peers and within central Amazon PXT and Talent teams. Medium term projects include: leading our organization through talent processes (Q1 is our busiest talent season), evolving approaches to talent management for Entertainment talent, developing relevant internal communications mechanisms, and rethinking and redesigning talent management for professional creatives.
About the team
This Executive Talent Lead will be part of the Entertainment PXT Talent Experience Team. Our purpose is to create and deliver talent programs that help us be Earth’s Best Employer by focusing on the employee experience – from joining Amazon Entertainment, to building connection with each other and our customers, through growing personally and professionally, to being empowered to balance fun, wellness, and learning to sustain a long, fulfilling career with Amazon.
BASIC QUALIFICATIONS
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- Bachelor's degree or equivalent in Business, Program Management, Human Resources, Employment Law, Computer Science, Finance, Computer Information Systems, Engineering, Operations Research or a related field
- 5+ years of HR, talent acquisition, management consulting, or project/program management experience
- Experience managing multiple projects and priorities across teams in a fast-paced, deadline-driven environment
- Experience in verbal and written communication for executive level leaders
- Proven track record of end-to-end ownership of operational process, change management, and communications.
PREFERRED QUALIFICATIONS
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- Knowledge of compensation/total rewards, talent management, performance management, compliance, and organization design and development
- HR or Talent Development project management experience
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, CA, Culver City - 125,800.00 - 170,200.00 USD annually
USA, WA, Seattle - 125,800.00 - 170,200.00 USD annually
Salary Context
This $125K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $170K across 217 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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Amazon.com, 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 $154,000 based on 8,743 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $225,000. Disclosed range: $125K to $170K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Amazon.com AI Hiring
Amazon.com has 17 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Seattle, WA, US, Sunnyvale, CA, US, Bellevue, WA, US. Compensation range: $117K - $350K.
Location Context
Across all AI roles, 7% (2,732 positions) offer remote work, while 34,484 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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