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DESCRIPTION
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Amazon Advertising is investing heavily in building AI\-native data infrastructure, and our team sits at the center of that transformation. We own the Ads Real\-Time Data Service and Advertiser Profiles: the systems that provide every advertising AI agent with immediate, structured access to advertiser context. Today, advertising agents and skills across Amazon depend on our platform to answer questions about campaign performance, budget allocation, brand positioning, and advertiser behavior within sub\-second latency. We process billions of data points daily with 1\-3 minute refresh cadences, serving millions of advertisers across all Amazon marketplaces worldwide.
Our team is part of Ads AI Core Infrastructure (ACI), the organization responsible for the foundational systems that make Amazon's advertising AI possible: knowledge bases, runtime environments, agent tooling, and the real\-time data layer that connects them all. The programs you will lead span the full lifecycle: from defining the data model and onboarding new advertiser signal types, to coordinating cross\-organization launches with agent teams, measurement systems, and advertising console experiences. You will own programs that touch 30\+ engineering teams across 10 VP organizations, drive alignment between science teams developing new embedding and retrieval techniques and engineering teams productionizing them, and manage the rollout of platform capabilities to advertising agents and skills across Amazon's global advertising business. The work is technically complex (distributed systems, real\-time streaming, large language model optimization), organizationally complex (multiple VP\-level stakeholders with competing priorities), and time\-sensitive (agent teams are shipping new advertiser experiences weekly and depend on our platform to keep pace).
Key job responsibilities
Program Strategy \& Roadmap Ownership
You own the multi\-quarter roadmap for Advertiser Data and Profiles across both our team and our partner teams. You define program milestones, success metrics, and delivery timelines. You write and present the program narrative for OP1/OP2 documents and monthly business reviews to Directors and VPs. You make data\-driven trade\-off recommendations when scope, resources, and timelines conflict, and you drive crisp decisions about what ships now versus what defers.
Cross\-Organization Program Delivery
You manage programs spanning our platform engineering team, applied science teams building embedding and retrieval models, agent builder teams consuming our APIs, measurement and attribution teams depending on our data, and advertising console teams surfacing profile data to advertisers directly. You create and maintain program schedules that account for cross\-team dependencies, negotiate resource commitments, and drive weekly execution cadence across all workstreams. When programs stall, you diagnose whether the block is technical, organizational, or resource\-based, and you drive resolution through the appropriate path.
Technical Program Leadership
You understand distributed systems architecture well enough to participate in design reviews, challenge assumptions about data freshness and consistency guarantees, and ensure that what we commit to delivering is technically feasible within the stated timeline. You work with engineers to decompose large programs into independently deliverable milestones. You ensure that real\-time ingestion pipelines, MCP server infrastructure, and profile computation systems meet their availability and latency commitments. You own the launch readiness process for platform features consumed by 30\+ downstream teams.
Operational Excellence \& Risk Management
You establish and maintain the mechanisms that keep programs healthy: status dashboards, weekly program reviews, escalation criteria, and stakeholder communication cadences. You proactively identify risks weeks before they become blockers and bring mitigation plans. You run retrospectives after major launches and drive the resulting improvements into team process. You own capacity planning coordination across our services during peak advertising seasons.
Stakeholder Communication \& Influence
You are the single\-threaded owner of program communication to senior leadership. You write documents that clearly articulate scope, timeline, risk, and trade\-offs for Directors and VPs. You present at monthly business reviews and operational reviews. You build consensus across teams with different priorities by finding the framing that connects each team's goals to the program outcome. You manage expectations proactively, ensuring leadership is never surprised by delays or scope changes.
A day in the life
Morning: Program Execution and Unblocking
Your day starts with the program dashboard showing 3 of 4 workstreams green and one yellow: the real\-time campaign ingestion pipeline is blocked on a schema change from the upstream data warehouse team. You write a concise status update to your Director with the issue, impact to timeline, and your proposed mitigation. You then join a 30\-minute stand\-up with your cross\-team leads where you confirm the data warehouse team has scheduled the schema migration for next sprint. You update the program tracker, adjust the downstream timeline by 3 days, and notify the agent teams that their new campaign signals onboarding date shifted.
Mid\-morning, you are in a design review with the platform engineers and an Applied Scientist. They are proposing a new context compression approach for high\-cardinality advertiser data that would reduce token consumption by 35% but requires changes to the MCP server response format. You ask questions about backward compatibility, migration path for existing consumers, and latency impact. You determine this needs a phased rollout and work with the team to define the phases and success criteria for each.
Afternoon: Strategic Planning and Stakeholder Alignment
After lunch, you are drafting the Q4 roadmap section for the OP1 narrative. You are framing the Advertiser Profiles expansion in terms of what it enables for advertisers (richer recommendations, faster onboarding to new ad products) rather than the technical components. You coordinate with the product manager on the customer\-facing narrative and with the science lead on which profile signals will be ready by launch.
You join a weekly sync with the three agent teams that are your largest consumers. They want real\-time access to advertiser creative assets as part of the profile. You assess the engineering cost, negotiate a phased delivery (metadata first, full assets in Q1\), and commit to a date for the first phase. You document the agreement and add the work items to the backlog.
Late Afternoon: Operational Reviews and Forward Planning
You run a 45\-minute program review with your Director and the partner team leads. You walk through each workstream: what shipped this week, what ships next week, where the risks are. One risk surfaces: the data warehouse team is planning a schema migration that could disrupt our ingestion pipelines during peak season. You escalate this to your VP with a clear ask (timing coordination) and a proposed resolution path.
Before end of day, you review the launch readiness checklist for next week's release of a new profile data type to all advertising agents. Load testing passed, documentation is complete, the rollback plan is reviewed. You approve the launch and send the go/no\-go communication to stakeholders.
About the team
Morning: Program Execution and Unblocking
Your AI\-powered program dashboard flags one workstream yellow overnight: a data warehouse schema change is blocking campaign ingestion. You use Kiro to draft the status update and mitigation plan for your Director, then join a 30\-minute stand\-up where you confirm the upstream team has committed to next sprint. Your coding agent auto\-updates the program tracker, adjusts dependent timelines, reprioritizes the sprint backlog for affected teams, and posts stakeholder notifications. By mid\-morning you are in a design review, free to focus on technical trade\-offs rather than administrative coordination.
Afternoon: Strategic Planning and Stakeholder Alignment
After lunch, you are drafting the Q4 roadmap section for the OP1 narrative. You are framing the Advertiser Profiles expansion in terms of what it enables for advertisers (richer recommendations, faster onboarding to new ad products) rather than the technical components. You coordinate with the product manager on the customer\-facing narrative and with the science lead on which profile signals will be ready by launch.
You join a weekly sync with the three agent teams that are your largest consumers. They want real\-time access to advertiser creative assets as part of the profile. You assess the engineering cost, negotiate a phased delivery (metadata first, full assets in Q1\), and commit to a date for the first phase. You document the agreement and add the work items to the backlog.
Late Afternoon: Operational Reviews and Forward Planning
You run a 45\-minute program review with your Director and the partner team leads. You walk through each workstream: what shipped this week, what ships next week, where the risks are. One risk surfaces: the data warehouse team is planning a schema migration that could disrupt our ingestion pipelines during peak season. You escalate this to your VP with a clear ask (timing coordination) and a proposed resolution path.
Before end of day, you review the launch readiness checklist for next week's release of a new profile data type to all advertising agents. Load testing passed, documentation is complete, the rollback plan is reviewed. You approve the launch and send the go/no\-go communication to stakeholders.
BASIC QUALIFICATIONS
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- 7\+ years of working directly with engineering teams experience
- 7\+ years of technical product or program management experience
- 5\+ years of software development experience
- Experience designing or architecting (design patterns, reliability and scaling) of new and existing systems
- Experience managing programs across cross functional teams, building processes and coordinating release schedules
- Experience owning/driving roadmap strategy and definition
PREFERRED QUALIFICATIONS
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- 8\+ years of hands\-on work managing complex technology projects experience
- Experience managing projects across cross functional teams, building sustainable processes and coordinating release schedules
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, Palo Alto \- 203,500\.00 \- 275,300\.00 USD annually
USA, NY, New York \- 194,700\.00 \- 263,400\.00 USD annually
USA, WA, SEATTLE \- 177,000\.00 \- 239,400\.00 USD annually
Salary Context
This $194K-$275K range is above the 75th percentile 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 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 $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 ($235K) sits 9% above the category median. Disclosed range: $194K to $275K.
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
AI roles in New York pay a median of $220,000 across 1,650 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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