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About This Role
DESCRIPTION
Are you excited about turning signal into action at scale? We are looking for a Senior Data & AI GTM Specialist to build and own the campaign execution arm that activates the AWS field the moment we detect a meaningful signal. Today our team synthesizes field, customer, and product signals into structured go-to-market recommendations. This role extends that capability by owning what happens next, converting a detected signal into a targeted, sequenced campaign that reaches the right field teams and the right customers within minutes to days rather than weeks or months.
Signals come from everywhere. Internally, they surface through seller conversations, deal blockers, product feature requests, , opportunity and pipeline data, and win and loss patterns. Externally, they surface through competitive moves, model launches, customer forum activity, and industry trends. Your job is to make sure that when a signal crosses a threshold worth acting on, a well-designed campaign fires automatically or near-automatically, with differentiated asks grounded in each account's context.
This is not traditional business development where you manage a handful of named accounts. Instead, you will design the mechanism that listens for signal, decides which signals warrant field activation, and orchestrates the outreach, enablement, and follow-through that turns that activation into pipeline. You will build this on top of our existing signal synthesis program and increasingly leverage agentic AI to move from manual triage toward an always-on detect-and-activate loop. You will own the full path from signal to seller action to measurable pipeline outcome.
You will be a deep expert in using AI to derive signal from noise and in using AI to execute on that signal at scale. This is the defining technical skill of the role. You will apply AI both to mine thousands of internal and external inputs for the patterns that matter and to turn those patterns into field activation faster and more precisely than any manual process could.
Key job responsibilities
Own the signal-to-activation mechanism end to end, defining which internal and external signals warrant field activations, campaigns, asset updates, or knowledge base improvements, and the closed-loop tracking that confirms the field acted and the signal was resolved
Design and launch targeted activation campaigns that convert a detected signal into a differentiated, sequenced field ask, reaching the right sellers and customers with context-aware messaging rather than undifferentiated broadcast
Build on the existing GTM signal synthesis capability, extending it from insight generation into campaign execution so that structured recommendations translate directly into field motion
Establish differentiated, cohort-based targeting that segments accounts by journey stage, existing pipeline, and prior actions, ensuring each seller receives an ask proportional to where their account already is
Ingest and normalize signal from diverse sources, including seller conversations, internal AI assistant prompts, deal blockers, product feature requests, opportunity data, competitive intelligence, model launches, and customer forums
Be a deep expert in using AI to derive signal and to execute on signal at scale, applying AI both to mine thousands of internal and external inputs for the patterns that matter and to convert those patterns into precise field activation faster than any manual process
Operate agentic AI mechanisms that mine signal in real time, prioritize what matters, and increasingly auto-generate the activation and recommendation, moving the team from manual triage toward an always-on loop
Drive internal teams to build and maintain the signal architecture, holding the source teams accountable for the pipelines, instrumentation, and data quality that keep signal flowing reliably from every internal and external source
Partner with Finance and Sales Operations to surface the top signals that are stalling deals in business reviews, quantifying blocked pipeline by root cause and turning that view into prioritized field action
Partner cross-functionally with the signal synthesis owner, product and service GTM teams, demand marketing, field enablement, sales leadership, and analytics to align activation campaigns to product priorities and field capacity
Define the business requirements for reporting that quantifies activation impact and blocked pipeline by root cause, giving leadership visibility into what signals drove which outcomes
Measure, report, and optimize activation campaigns against pipeline generated, conversion rate, deal velocity, signal resolution time, and revenue influence, presenting results to senior leadership
Continuously improve the mechanism, running experiments on signal thresholds, targeting logic, messaging, and channels so activation gets sharper and faster over time
A day in the life
You start your morning reviewing the overnight signal queue. Your AI mining mechanism has flagged a spike in field conversations and internal AI assistant prompts referencing a competitor's new product launch. You assess whether it clears the threshold for a field activation, confirm it does, and begin shaping the campaign. You define the target cohort using existing account intelligence, so that sellers already engaged on the topic get a lighter validate-and-advance ask while sellers with no engagement get the full play.
Mid-morning, you meet with Finance and Sales Operations to review the top deal blockers stalling pipeline this month. You work through which blockers trace back to product gaps, unlaunched models, or competitive pressure, quantify the blocked pipeline behind each, and decide which ones warrant a field activation or an escalation to the owning product team. You also spend time with the internal teams that own the upstream signal sources, holding them accountable for the instrumentation and data quality that keep signal flowing reliably.
After lunch, you work with the product GTM lead for a service approaching a major milestone. You align on positioning and which customer segments to prioritize, then wire the launch into your activation mechanism so that the moment the signal goes live, the field receives a coordinated ask rather than a scattered one. You also review how your agentic mining agent performed on last week's signals, tuning its prioritization so the highest-value patterns rise to the top automatically.
Later in the afternoon, you pull together a metrics read-out for leadership. You show pipeline influenced by activation, signal resolution time, and how differentiated targeting is outperforming broadcast on both engagement and deal size. You call out one activation exceeding targets and recommend scaling it, and you flag a signal source that is generating noise rather than value and propose refining its threshold.
Before wrapping up, you spend time in the field channels where sellers are responding to your latest activation. A few surface customer stories and blockers worth capturing. You feed those back into the mechanism to sharpen the next activation and to ensure each blocker gets a tracked response, closing the loop between what the field told us and what we did about it.
About the team
We are the Data & AI GTM Acceleration team within Amazon Web Services. Our mission is to help every AWS customer realize the value of AI and data services faster. We build and operate go-to-market programs and initiatives that connect the right customer with the right solution at the right moment, at a scale that a traditional sales model cannot reach on its own.
We sit at the center of a broad ecosystem. On one side, we work with AWS product and GTM teams building AI and data services. On the other, we partner with thousands of field sellers, solution architects, and inside sales professionals who bring those services to customers. Our job is to bridge the gap between what the field is telling us and what the field does next, turning signal into coordinated action.
What makes this team unique is how we operate. We build, we prove, and we scale. We prototype new go-to-market mechanisms, validate them with real sellers and real customers, and then hand proven playbooks to enterprise scaling partners who deploy them across the global AWS field. We move quickly, experiment often, and measure everything. We are increasingly AI-native in how we work, building and operating agents that mine field signal and deliver personalized recommendations directly into seller workflows.
Culturally, we are builders obsessed with measurable impact. We operate with a high degree of autonomy, trust each other to own outcomes end to end, and maintain a bias for action even when the path forward is not fully defined. You will work alongside people who are curious, technically fluent, collaborative across organizational boundaries, and energized by the pace of change in AI.
BASIC QUALIFICATIONS
- 5+ years of Go-To-Market, Business Development, Sales, or Consulting experience
- 5+ years of working with Data & AI related technologies, including, but not limited to, AI/ML, GenAI, Analytics, Database, and/or Storage experience
- 5+ years of in program management, workforce strategy development, or metrics-driven decision making experience
- Bachelor's degree
- Experience selling enterprise software or cloud-based applications
- Experience explaining complex technical concepts to various business and technical audiences
- Experience presenting to both technical and non-technical executive audiences
- Experience leading complex, multi-year initiatives that may be cross-functional and/or span business and technology
PREFERRED QUALIFICATIONS
- Master's degree in business, data science, public administration, finance, engineering, human resources, or related field, or PMP certificate
- Experience interpreting data and making business recommendations
- Experience identifying, negotiating, and executing complex legal agreements
- Experience interpreting data and making business recommendations across leadership and cross-functional teams
- Experience using analytical tools for workforce metrics and reporting
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.
Pursuant to the San Francisco 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, Mountain View - 162,700.00 - 220,200.00 USD annually
USA, CA, San Diego - 147,900.00 - 200,100.00 USD annually
USA, CA, San Francisco - 162,700.00 - 220,200.00 USD annually
USA, GA, Atlanta - 147,900.00 - 200,100.00 USD annually
USA, MA, Boston - 147,900.00 - 200,100.00 USD annually
USA, NY, New York - 162,700.00 - 220,200.00 USD annually
USA, TX, Austin - 147,900.00 - 200,100.00 USD annually
USA, TX, Dallas - 147,900.00 - 200,100.00 USD annually
USA, VA, Arlington - 147,900.00 - 200,100.00 USD annually
USA, WA, Seattle - 147,900.00 - 200,100.00 USD annually
Salary Context
This $147K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($174K) sits 18% below the category median. Disclosed range: $147K to $200K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Amazon Web Services AI Hiring
Amazon Web Services has 71 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Research Engineer, Data Scientist. Positions span New York, NY, US, Bellevue, WA, US, Denver, CO, US. Compensation range: $97K - $299K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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