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About This Role
DESCRIPTION
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AWS is looking for a Sr. Partner Marketing Manager to own the worldwide marketing relationship with our most strategic AI Labs and frontier model provider partners. You will be the connective tissue between AWS's AI product and service teams and partners such as Anthropic, OpenAI, Meta AI, Mistral, and Cohere \- translating technology roadmaps into joint go\-to\-market programs that accelerate customer adoption and drive measurable pipeline.
This role sits within the AI \& Startup Partner Marketing team, part of the AWS Partner Marketing organization working closely with the Data \& AI Partner GTM team. The partners you work with are not traditional ISVs \- they are category\-defining companies at the frontier of foundation model research whose models power workloads across Amazon Bedrock and the broader AWS AI stack. Your job is to build the marketing engine that helps these partners succeed on AWS while helping AWS customers access their capabilities through joint messaging, campaigns, launches, and programs.
You will develop joint marketing plans (JMPs), orchestrate cross\-functional activation across AWS field sales, product marketing, and partner development, and own pipeline and attribution metrics for your assigned partner portfolio. The right candidate combines deep marketing craft with enough AI/ML literacy to hold their own in technical product conversations, and enough commercial instinct to prioritize programs by revenue impact. This portfolio will evolve as the frontier AI landscape shifts \- we need someone comfortable building structure in ambiguity while staying close to where the technology and customer demand is heading.
Key job responsibilities
- Develop and execute worldwide joint marketing plans (JMPs) for assigned frontier model provider partners, aligned to AWS AI service priorities and customer adoption goals.
- Define partner\-specific value propositions and joint positioning that differentiates the partner's offering on AWS relative to competing platforms.
- Translate product launches and feature releases (e.g. new model availability on Amazon Bedrock, AgentCore integrations) into partner\-led demand generation motions.
- Design, launch, and optimize multi\-channel campaigns (digital, events, content, ABM) that drive joint pipeline for both AWS and the partner.
- Own Marketing Development Fund (MDF) planning and execution for your partner portfolio; build business cases for incremental investment where opportunity warrants.
- Coordinate partner presence at AWS flagship events (re:Invent, Summits, Builder Days) and partner\-hosted events, advising on messaging, audience targeting, and customer engagement tactics.
- Create partner activation playbooks and enablement assets (messaging guides, battle cards, solution briefs) that equip AWS field teams and partner sellers.
- Serve as the primary marketing point\-of\-contact for assigned partners, building trusted relationships at senior marketing and executive levels within partner organizations.
- Align closely with Partner Development Managers (PDMs), Solutions Architects, AWS Product Marketing, and Service GTM teams to ensure marketing programs reinforce co\-sell plays and technical readiness.
- Collaborate with regional partner marketing teams to scale worldwide programs into local execution while maintaining message consistency.
\- Set clear KPIs \- partner\-sourced pipeline, marketing\-influenced revenue, MDF ROI, activation milestones \- and report progress to leadership.
- Track and report on pipeline contribution, campaign ROI, MDF utilization, and co\-sell revenue; use data to continuously optimize partner marketing performance.
- Prepare monthly business reviews (MBRs) and quarterly partner scorecards for leadership review.
A day in the life
You might start your morning reviewing campaign performance data for your partner portfolio, then jump into a strategy session with a partner's marketing team to align messaging ahead of a model launch on Bedrock. Later, you could be working with AWS Product Marketing to develop positioning for a joint solution brief, or coordinating with field teams on an account\-based play targeting enterprise customers evaluating foundation model providers. You might wrap up by joining a cross\-functional planning call for re:Invent partner activations \- ensuring your partners have the right presence, the right story, and the right audience. No two days look the same, but every day centers on connecting partners with customers through compelling marketing that drives measurable business outcomes.
About the team
The AI \& Startup Partner Marketing team is a worldwide team within AWS Partner Marketing. We are the marketing engine for AWS's most strategic AI relationships \- spanning frontier model providers, AI platform companies, and high\-growth startups building on AWS AI services.
Our team works at the intersection of partner innovation and AWS's most rapidly evolving service areas: Amazon Bedrock, AgentCore, Amazon SageMaker, Amazon Q, and the broader generative AI stack. We work with category\-defining companies \- from the largest AI labs in the world to emerging startups \- building joint go\-to\-market programs that drive customer adoption and revenue.
We value marketers who combine strategic thinking with execution intensity, can navigate ambiguity in a fast\-moving domain, and measure their success by the pipeline they create rather than the programs they run. This is a builder role in a builder team \- you will be creating playbooks and motions that do not yet exist.BASIC QUALIFICATIONS
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- 6\+ years of professional non\-internship marketing experience
- 7\+ years of developing and managing acquisition marketing or channel programs experience
- Experience using data and metrics to drive improvements
- Experience building, executing and scaling cross\-functional marketing programs
- Experience developing and executing campaigns across a multitude of timezones and languages
PREFERRED QUALIFICATIONS
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- Experience driving direction and alignment with large cross\-functional teams and agency partners
- Experience designing and executing joint marketing plans with strategic alliance partners with global footprint
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
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, WA, Seattle \- 118,200\.00 \- 160,000\.00 USD annually
Salary Context
This $118K-$160K 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 Amazon Web Services, 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 ($139K) sits 35% below the category median. Disclosed range: $118K to $160K.
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 Web Services AI Hiring
Amazon Web Services has 93 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer, AI Software Engineer, AI Product Manager. Positions span New York, NY, US, Arlington, VA, US, Cupertino, CA, US. Compensation range: $160K - $350K.
Location Context
AI roles in Seattle pay a median of $228,700 across 516 tracked positions. That's 6% above 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 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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