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About This Role
DESCRIPTION
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Shape the future of cinematic storytelling: for creators, by creators. We're seeking a founding Principal Product Marketing Manager to own how the industry understands our GenAI\-enabled creative platform — built at Prime Video and Amazon MGM Studios to help professional creators bring animation, live action, and VFX content to screen. AI Studios is a fast\-growing team operating as a startup within Amazon, with direct ownership of studio\-level goals.
This is a founding role: you will build the product marketing function for AI Studios from the ground up and own its strategy end to end. You are accountable for how our platform is positioned, differentiated, and understood at industry scale — the narrative, competitive positioning, and market brand that drive awareness and demand across Amazon MGM Studios, AWS, and the broader creative industry. You will operate with complete independence in a space where the long\-term strategy is not yet defined, set the marketing strategy, and bring the data\-driven judgment to make the hard positioning trade\-offs. The single most important thing you bring is credibility in the AI content\-creation industry: you know the tools, you have made content with them, and you can hold your own in any room — with the best creators and filmmakers, and with the most skeptical executives.
Key job responsibilities
As AI Studios' founding Principal Product Marketing Manager, you will define and own the product marketing strategy for the platform, with long\-term impact on how it is perceived across the industry. You will:
- Own the platform's core positioning and value proposition — the messaging and go\-to\-market narrative that define what it is, who it's for, and why it matters — so the industry, customers, and partners understand it clearly and consistently.
- Own competitive differentiation: track a fast\-moving landscape, articulate where we lead and where we are heading, and evolve the story as the market and our capabilities change.
- Aggregate and synthesize signals — competitive intelligence, customer and creator needs, industry and model trends — into a coherent marketing strategy and point of view that others build on.
- Build the definitive narrative and asset portfolio, versioned for distinct audiences (executive, artist and creative, technical and model\-focused) and kept current with our latest capabilities, so the organization is always ready to represent the platform at its best.
- Drive awareness and demand across internal Amazon (Prime Video, Amazon MGM Studios, AWS, AGI, etc.) and external stages, converting broad interest into a durable pipeline of engagement rather than one\-off scrambles.
- Represent the platform credibly in high\-stakes rooms — and equip senior leaders with the framing, competitive answers, and visual aids they need to carry the story themselves.
- Partner with Business Development and go\-to\-market teams to shape how the platform is positioned for the customer segments it serves over time.
- Create the mechanisms that let product marketing scale as the function grows, force\-multiplying your impact through creative partners and the broader team rather than producing everything yourself.
- Collaborate closely with your cross\-functional counterparts, including Product Management, UX Design, Engineering, Applied Science, and Creative teams to understand and inform the direction of our product suite.
A day in the life
Your morning starts with a request from leadership for a tailored pitch ahead of an executive meeting — and because you own a current, coherent narrative, you turn it around without pulling anyone off their day job. You sync with a creative lead to capture a fresh demo and usage anecdotes, then sharpen the competitive framing on where the platform leads and where it is heading. After lunch, you prep a model\-focused cut of the story with Science for a technical audience and a VP\-level cut for an upcoming review, and align with Business Development on how the positioning maps to their go\-to\-market. You field inbound interest from another team and fold it into the demand plan. You close the day evolving the differentiation story against the latest competitor release. Throughout, you're the person who owns how the industry sees what we're building.
About the team
AI Studios is a small, high\-ownership "startup within Amazon," formed by Prime Video and Amazon MGM Studios to build multimodal AI tools and models for content creation with human's in the driver's seat. We work directly with professional filmmakers, animators, and VFX artists on active productions. We operate from first principles, learn directly from working creators on real productions, and hold a high bar for craft, safety, and storytelling magic. If you're motivated by invention and inspired by storytellers, this is your seat at the table.BASIC QUALIFICATIONS
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- 9\+ years of professional non\-internship marketing experience
- Experience communicating results to senior leadership
- Experience using data and metrics to drive improvements
- Experience building, executing and scaling cross\-functional marketing programs
- Experience driving direction and alignment with cross\-functional teams
- Experience with Excel or Tableau (data manipulation, macros, charts and pivot tables)
PREFERRED QUALIFICATIONS
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- Experience with creating GTM initiatives at scale
- Experience working cross\-functionally across marketing, product management, analytics, UX, and research
- Experience and credibility in the AI content\-creation or media\-production industry
- Hands\-on fluency creating content with generative\-AI tools
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 \- 170,000\.00 \- 230,000\.00 USD annually
Salary Context
This $170K-$230K range is above 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 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 $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 ($200K) sits 7% below the category median. Disclosed range: $170K to $230K.
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.
Amazon.com AI Hiring
Amazon.com has 122 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, AI Product Manager, AI Software Engineer. Positions span Seattle, WA, US, Santa Clara, CA, US, New York, NY, US. Compensation range: $128K - $338K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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