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About This Role
DESCRIPTION
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Amazon Web Services (AWS) Nonprofit Business (NPO) is seeking a research industry expert and strategic advisor with deep research background combined with expertise in advanced cloud services for research computing. This person will serve as an advisor and executive\-level relationship builder across nonprofit research institutions and other similarly advanced customers with complex AI, model\-building, and high\-performance computing use cases—driving adoption of AWS's advanced compute, AI/ML, and HPC services for research workloads.
The focus of this role is on leveraging cloud and advanced AI services to accelerate research and advance mission impact. The Strategic Advisor will operate across the NPO business as a shared strategic resource, building executive relationships at the C\-suite, Chief Research Officer, and Principal Investigator level; influencing research computing policy (including NIH and NSF funding mechanisms); generating qualified pipeline across NPO Research territories and other similarly advanced accounts; and positioning AWS as the platform of choice for next\-generation research computing.
This role builds the executive\-level and industry\-wide presence that opens doors, shapes market perception, and creates net\-new demand across the portfolio. This position benefits all territories within the Nonprofit Research team as well as other NPO accounts with comparably advanced technical workloads, amplifying impact across the business.
Key job responsibilities
1\. Strategic Advisory \& Strategic Engagement
Build trusted relationships at the C\-suite, Chief Research Officer, and Principal Investigator level across research institutions and other similarly advanced NPO customers with complex AI/ML and model\-building use cases.
Conduct executive listening sessions and strategic workshops to expose organizational goals, current structures, blind spots, and drive alignment with measurable accountability
Maintain tool\-agnostic credibility to access senior decision\-makers; serve as a trusted, objective resource for executive leaders
Build and maintain a set of active, deep executive relationships across NPO Research territories and other advanced accounts
Design and facilitate executive engagement programs (cohorts, roundtables, advisory boards) spanning both research institutions and advanced AI/ML customers
2\. Advanced Cloud Services for Research \& AI/ML
Position AWS compute, storage, AI/ML, and HPC services as the platform for next\-generation research computing — genomics pipelines, climate modeling, computational biology, AI for scientific discovery.
Develop and maintain deep expertise across research computing domains and advanced AI/ML architectures: genomics, computational biology, climate modeling, AI/ML for science, research data platforms, and large\-scale model training
Proactively identify industry trends and position AWS to anticipate customer needs (e.g., AI for research, data platforms for climate science, high\-performance computing for genomics, foundation model fine\-tuning)
Define and execute go\-to\-market strategy for nonprofit research institutes and advanced AI/ML customers aligned with AWS business plans and priorities
Serve as the specialist advisory resource when NPO accounts require deep technical industry\-specific credibility for complex AI, model\-building, or HPC workloads
3\. Thought Leadership \& Industry Presence
Deliver presentations at research events and industry conferences; contribute to advisory boards; publish through industry\-appropriate channels (academic conferences, research publications, professional forums, LinkedIn)
Maintain credibility within the research community and among advanced AI/ML practitioners through continued scholarly and technical engagement
Contribute to research advisory board presence and seek committee participation that provides direct influence over research computing and AI adoption
Present to leadership, mapping research industry insights and emerging AI/ML trends and applications in the nonprofit sector to business value
4\. Pipeline Development \& Business Growth
Generate qualified pipeline across NPO Research territories and other similarly advanced accounts through proactive business development
Identify and develop new workload opportunities in pipeline — including AI/ML model training, HPC migration, and cloud\-native research data platforms
5\. Policy \& Funding Ecosystem
Cultivate relationships with NIH, NSF, and other federal research funding organizations
Advocate for cloud computing in research funding mechanisms; initiate and influence active policy discussions
Evaluate existing grants and proactively engage PIs to offer assistance with cloud\-based solution designs
6\. Cross\-Functional Collaboration
Partner closely with Account Managers across all territories via joint account planning; coordinate with SAs to convert strategic pipeline into revenue
Work with partnership, program, and marketing teams to create and execute strategic account plans
Aggregate insights from executive engagements to inform new solutions, sales motions, and campaigns
Serve as a bridge between Research territories, SA team, and other NPO teams, ensuring advanced AI/ML expertise is accessible across the organization.
A day in the life
Your morning starts with a Chief Research Officer listening session at a biomedical research institute exploring HPC migration. Midday, you join an account planning call with an Account Manager and SA to advance a genomics pipeline opportunity. After lunch, you deliver a keynote at a virtual research computing symposium, then prep a workshop for a climate nonprofit scaling AI\-driven modeling on GPU clusters. You close the day reviewing an NSF funding mechanism and advising a PI on a cloud\-native grant proposal — opening doors that drive pipeline across NPO.
About the team
The AWS Nonprofit Business team helps nonprofit organizations harness cloud to advance their missions — from modernizing constituent engagement to deploying AI\-powered solutions that amplify impact. Within this team, Nonprofit Research supports premier research institutions across biomedical, climate, policy, and applied sciences. These organizations face significant barriers: uncertain funding, evolving compliance, multidisciplinary demands, and talent retention challenges. AWS helps overcome them with high\-performance computing, scalable storage, secure collaboration, and AI/ML tools — enabling researchers to focus on discovery rather than infrastructure.
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
Why AWS?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
AWS values curiosity and connection. Our employee\-led and company\-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do.
Mentorship \& Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge\-sharing, mentorship and other career\-advancing resources here to help you develop into a better\-rounded professional.
Work/Life Balance
We value work\-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.
BASIC QUALIFICATIONS
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- Master's degree, or PhD or equivalent research experience
- 10\+ years of direct work experience with or in nonprofit research institutions, academic research organizations, federally funded research programs, or organizations with comparably advanced AI/ML and computational workloads
- Demonstrated presence in the research or advanced AI/ML community — evidenced by publications, conference presentations, advisory board participation, or professional society membership
- Deep understanding of research computing paradigms including HPC, genomics pipelines, AI/ML for scientific discovery, large\-scale model training, and cloud\-based research data platforms
- Familiarity with research funding ecosystems (NIH, NSF, private foundations) and understanding of grant\-funded technology procurement
PREFERRED QUALIFICATIONS
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- Ph.D. in a life sciences, computational, or physical sciences discipline strongly preferred
- Recognized name within the nonprofit research community or advanced AI/ML community — known and respected by peers as a thought leader and practitioner
- Prior experience as a Principal Investigator, Research Director, Department Head, or equivalent research/technical leadership role
- Experience with cloud\-based research computing architectures, AI/ML model training at scale, and digital transformation in research or advanced computing settings
- Existing relationships with leadership at major research funders (NIH, NSF, Gates Foundation, Chan Zuckerberg Initiative, Howard Hughes Medical Institute, or equivalent)
- Track record of influencing research policy or funding mechanisms at the institutional level
- Experience designing and delivering executive engagement programs (cohorts, roundtables, advisory boards)
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, NY, New York \- 162,700\.00 \- 220,200\.00 USD annually
Salary Context
This $162K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($191K) sits 12% below the category median. Disclosed range: $162K to $220K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Amazon Web Services AI Hiring
Amazon Web Services has 73 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Research Scientist, Data Scientist. Positions span New York, NY, US, Austin, TX, US, Jersey City, NJ, US. Compensation range: $129K - $342K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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