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About This Role
DESCRIPTION
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We are seeking a Senior Applied Scientist to pioneer the application of artificial intelligence and machine learning to cyber threat intelligence at Amazon scale. In this role, you will invent and deploy novel AI/ML systems that automate threat detection, accelerate intelligence analysis, and enable proactive defense capabilities. You will work on ambiguous, scientifically\-complex problems where traditional engineering approaches fall short—from building predictive models that score threat likelihood against Amazon's specific attack surface, to developing graph neural networks that cluster adversary infrastructure, to creating LLM\-powered systems that multiply analyst productivity.
This is a unique opportunity to bring scientific rigor to one of the most consequential problem domains in technology—protecting hundreds of millions of customers and the infrastructure that powers the global economy. You will be the first Applied Scientist embedded within ACTI, establishing the science agenda and building the foundation for AI\-driven threat intelligence at Amazon.
This position requires that the candidate selected be a US Citizen.
Key job responsibilities
Invent
- Identify, frame, and solve scientifically\-complex threat intelligence problems where no textbook solutions exist—including threat scoring, malware classification, infrastructure clustering, and intelligence automation
- Drive the scientific agenda for AI/ML within ACTI by proposing research initiatives, defining success metrics, and securing management buy\-in
- Extend and invent machine learning techniques for cybersecurity applications, including anomaly detection on noisy data, few\-shot learning for emerging threat families, and graph\-based reasoning over attacker infrastructure
- Publish research at peer\-reviewed venues (e.g., USENIX Security, IEEE S\&P, ACM CCS, NeurIPS workshops)
Implement
- Design, build, and deploy production AI/ML systems that process threat data at scale—from model training on petabyte\-scale security logs to real\-time inference serving millions of predictions daily
- Partner with ACTI engineering teams to integrate AI/ML models into existing intelligence platforms
- Develop end\-to\-end solutions including data pipelines, feature engineering, model training, evaluation frameworks, and production monitoring
- Write production\-quality code and deploy models with operational excellence—reliability, maintainability, and cost efficiency
Influence
- Influence across multiple ACTI sub\-teams and partner organizations
- Build consensus on scientific approaches, balancing analytical rigor with operational urgency inherent to threat intelligence
- Mentor security engineers and analysts on AI/ML concepts, helping the broader ACTI team develop data literacy and scientific thinking
- Represent ACTI in Amazon's internal science community and contribute to the broader information security research ecosystem
About the team
Amazon Cyber Threat Intelligence (ACTI) is responsible for identifying, curating, and reporting timely, accurate, and actionable threat intelligence to protect Amazon's global businesses and customers. We investigate, analyze, and defend against sophisticated cyber threats across all Amazon business lines—AWS, retail, entertainment, logistics, and corporate infrastructure. Our intelligence products serve Amazon and AWS leadership, service teams, partners, and both internal and external customers.
ACTI operates within Amazon's broader security organization led by the Chief Security Officer. We deliver intelligence that enables proactive defense, informs security investment decisions, and supports incident response across the world's largest cloud infrastructure.
Diverse Experiences
Amazon Security values diverse experiences. Even if you do not meet all of the 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 Amazon Security?
At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores.
Inclusive Team Culture
In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices.
Training \& 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, training, 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 flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.BASIC QUALIFICATIONS
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- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
- 5\+ years of relevant, broad research experience after PhD (or equivalent body of work demonstrating scientific innovation)
- Experience deploying AI/ML models into production systems with direct, verified customer impact
- Experience in one or more: NLP/LLMs, graph neural networks, anomaly detection, deep learning, or probabilistic modeling
- Software development skills
- Publication record (including NeurIPS, ICML, ICLR, ACL, EMNLP, KDD, USENIX Security, IEEE S\&P, or equivalent)
PREFERRED QUALIFICATIONS
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- Experience applying AI/ML to cybersecurity problems
- Ability to independently frame ambiguous problems, define research agendas, and deliver results with limited guidance
- Familiarity with threat intelligence frameworks and security operations concepts
- Experience with large\-scale graph analytics, knowledge graphs, or graph neural networks
- Experience building and deploying LLM/GenAI applications (RAG systems, fine\-tuning, prompt engineering at scale)
- Familiarity with ML frameworks such as PyTorch, TensorFlow, or JAX)
- Proficiency with AWS services (SageMaker, Bedrock, EMR, Glue, Lambda, S3\)
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, MD, Annapolis Junction \- 167,100\.00 \- 226,100\.00 USD annually
USA, NY, New York \- 183,800\.00 \- 248,700\.00 USD annually
USA, TX, Austin \- 167,100\.00 \- 226,100\.00 USD annually
USA, VA, Arlington \- 167,100\.00 \- 226,100\.00 USD annually
USA, WA, Seattle \- 167,100\.00 \- 226,100\.00 USD annually
Salary Context
This $167K-$248K range is above the median for Research Scientist roles in our dataset (median: $196K across 93 roles with salary data).
Role Details
About This Role
Research Scientists push the boundaries of what AI can do. They design experiments, develop novel architectures, publish papers, and translate research breakthroughs into production capabilities. This is where the fundamental advances happen, from attention mechanisms to diffusion models to reasoning chains.
The work is intellectually demanding and often ambiguous. You might spend months on an approach that doesn't pan out. The best research scientists combine deep mathematical intuition with engineering pragmatism. They know when to go deep on theory and when to run experiments. They read papers voraciously and can spot incremental contributions from genuine breakthroughs.
Across the 3,824 AI roles we're tracking, Research Scientist positions make up 3% of the market. At Amazon.com, this role fits into their broader AI and engineering organization.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
What the Work Looks Like
A typical week includes: reading and discussing recent papers with your team, designing and running experiments on multi-GPU clusters, analyzing results and iterating on hypotheses, writing up findings for internal review or publication, and collaborating with engineering teams to productionize promising results. The ratio of thinking to coding is higher than in engineering roles.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
Skills Required
PhD strongly preferred for most roles. Deep expertise in a specific area (NLP, computer vision, reinforcement learning, multimodal) is expected. PyTorch is the standard. Publication track record matters. Strong mathematical foundations in linear algebra, probability, optimization, and information theory are assumed.
Beyond the fundamentals, companies value experience with large-scale distributed training, novel architecture design, and the ability to bridge theory and practice. Understanding of current frontier topics (reasoning, multimodal, long-context, alignment) is essential. Code quality matters more than many researchers expect. Labs want researchers who can implement their ideas cleanly.
Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
Compensation Benchmarks
Research Scientist roles pay a median of $223,400 based on 223 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($207K) sits 7% below the category median. Disclosed range: $167K to $248K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Amazon.com AI Hiring
Amazon.com has 98 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, AI Product Manager, Data Scientist. Positions span New York, NY, US, Seattle, WA, US, Sunnyvale, CA, US. Compensation range: $101K - $300K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% above the national median.
Career Path
Common paths into Research Scientist roles include PhD Student, Research Engineer, Postdoc.
From here, career progression typically leads toward Research Lead, Distinguished Scientist, VP of Research.
The PhD is the entry point for most paths. Choose your advisor and research area carefully since they'll define your first industry position. Publish consistently, contribute to open-source projects in your area, and build relationships at conferences. Industry research offers better compensation and compute resources than academia, but the pressure to show product impact is real.
What to Expect in Interviews
Research interviews are multi-stage: a research talk (present your best paper), technical deep-dives on your methodology, and often a 'research proposal' exercise where you design an experiment to test a hypothesis. Coding rounds test implementation ability alongside theoretical knowledge. Be prepared to implement a paper from scratch and discuss the design choices the authors made. Strong candidates can critique papers constructively and identify gaps in experimental methodology.
When evaluating opportunities: Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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