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About This Role
DESCRIPTION
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We are looking for a Principal Applied Scientist to own and advance the scientific vision for WorkSpaces Advisor — our agentic AI system that serves as an always\-on troubleshooting companion for workspace administrators and end users. You will define the technical roadmap that transforms Advisor from a recommendation engine into a fully autonomous agent capable of reasoning across complex system states, orchestrating multi\-step remediation workflows, and continuously learning from outcomes.
This is a leadership role requiring someone who can set the scientific direction for agentic AI in the troubleshooting domain, drive breakthroughs in reasoning and planning under uncertainty, and build the ML foundations that make Advisor the most trusted AI companion in enterprise workspace management.
You'll define and drive the scientific strategy for Advisor's agentic capabilities, establishing the research agenda that keeps us at the frontier of autonomous troubleshooting and self\-healing systems.
Architect agentic reasoning systems that enable Advisor to autonomously diagnose root causes across complex, multi\-signal environments — correlating performance telemetry, session behavior, network conditions, and infrastructure state to identify problems before users feel them.
Design and build planning and orchestration frameworks that allow Advisor to compose multi\-step remediation actions, reason about dependencies and risks, and execute recovery workflows with appropriate human\-in\-the\-loop guardrails.
Develop advanced causal inference models that move beyond correlation to true root\-cause identification, enabling Advisor to distinguish symptoms from underlying issues across interconnected system layers.
Build continuous learning systems where Advisor improves from every interaction — leveraging reinforcement learning from human feedback (RLHF), outcome\-driven reward signals, and retrieval\-augmented generation (RAG) to expand its troubleshooting knowledge over time.
Pioneer natural language reasoning capabilities that allow Advisor to explain its diagnostic process, communicate findings clearly to administrators, and engage in collaborative problem\-solving dialogue.
Establish evaluation frameworks and safety mechanisms that ensure Advisor's autonomous actions maintain customer trust — defining confidence thresholds, escalation policies, and rollback strategies for automated remediation.
Influence the broader organization's AI strategy by identifying opportunities to extend Advisor's agentic patterns to adjacent problem spaces, and by publishing findings that advance the state of the art in autonomous IT operations.
Key job responsibilities
- Set the scientific vision and long\-term research agenda: Define what "best\-in\-class agentic troubleshooting" looks like scientifically, identify the key unsolved problems, and chart a multi\-year path to solving them — securing buy\-in from VP\-level leadership.
- Deliver breakthrough solutions on highly ambiguous problems: Independently identify, frame, and solve novel research challenges in agentic AI for troubleshooting — problems where neither the approach nor the success criteria are pre\-defined.
- Influence and align across the organization: Drive scientific alignment across product, engineering, and business teams. Translate complex ML concepts into actionable product strategy. Represent the science team in leadership forums and planning cycles.
- Build and elevate scientific excellence: Mentor scientists and engineers across the team. Establish best practices for experimentation, evaluation, and deployment of agentic systems. Set the standard for scientific rigor and code quality.
- Deliver end\-to\-end production systems with outsized business impact: Own the full lifecycle from research to deployment for Advisor's core intelligence — making pragmatic trade\-offs between long\-term invention and near\-term delivery while ensuring measurable customer and business outcomes.
- Advance the state of the art: Contribute to the external scientific community through publications, patents, and engagement that positions AWS as a leader in autonomous AI operations — bringing outside\-in innovation back into Advisor.
About the team
AWS is on a mission to transform how businesses operate by delivering intelligent, cloud\-powered applications. Our Applied AI Solutions organization accelerates customer success through intuitive, differentiated technology that solves enduring business challenges — blending vision with real\-world expertise to build turnkey solutions that are easy to adopt and built to scale.
Within this organization, we are building the next generation of secure, intelligent workspaces — environments purpose\-built for human\-AI collaboration at enterprise scale. Our WorkSpaces Advisor is an AI\-powered troubleshooting companion that proactively detects, diagnoses, and resolves workspace issues, transforming reactive IT support into intelligent, autonomous problem\-solving.BASIC QUALIFICATIONS
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- 5\+ years of hands\-on work in predictive modeling and analysis experience
- PhD in Electrical Engineering, Computer Science, Mathematics, or a related technical field
- Experience working in predictive modeling and analysis
- Experience distilling informal customer requirements into problem definitions, dealing with ambiguity and competing objectives
- Experience programming in Java, C\+\+, Python or related language
- Experience with leading experienced scientists as well as having a record of developing junior members from academia or industry to a career track in a business environment
PREFERRED QUALIFICATIONS
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- 10\+ years of relevant work in industry or academia experience
- Knowledge of problem solving, algorithm design and complexity analysis
- Experience creating novel algorithms and advancing the state of the art
- Have peer\-reviewed scientific contributions in premier journals and conferences
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.
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 3,708 AI roles we're tracking, Research Scientist positions make up 3% of the market. At Amazon Web Services, 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 $222,200 based on 197 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
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 Seattle pay a median of $236,900 across 267 tracked positions. That's 9% 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 3,708 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).
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 3,708 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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