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About This Role
DESCRIPTION
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Description
We are seeking a Senior UX Researcher to help shape the future of AI\-native business applications at AWS. In this role, you will lead high\-impact research across a product area, helping teams understand customer needs, evaluate emerging concepts, and improve live experiences. Your work will reduce ambiguity, de\-risk product and design decisions, and increase confidence that we are building solutions customers will adopt, trust, and find valuable.
You will partner closely with Design, Product, Engineering, and Applied Science to ensure customer evidence informs both near\-term product decisions and longer\-term direction. You will lead tactical and foundational research in ambiguous spaces, apply strong methodological judgment, and translate complex findings into clear recommendations that influence roadmaps, priorities, and customer outcomes.
This role is a strong fit for a researcher who can independently lead complex programs, shape the research approach for a broader problem space, and raise the quality of decision\-making through credible, actionable insight.
About you
You are an experienced researcher with strong customer instinct, sound judgment, and the ability to operate independently in complex problem spaces. You know how to identify the most important questions, define the right research path, and generate insights that meaningfully influence product direction. You are comfortable balancing speed and rigor, navigating ambiguity, and helping teams make confident decisions when the path forward is not yet clear.
You are skilled at leading end\-to\-end research, connecting multiple signals into a coherent point of view, and helping teams understand the implications of what they learn. You care not just about producing insights, but about shaping better products and stronger outcomes for customers and the business. You are also motivated to elevate the work around you through mentorship, thought partnership, and high standards for research craft.
Key job responsibilities
- Lead end\-to\-end research programs across a product area, including study design, recruitment, moderation, analysis, synthesis, and communication of findings
- Drive a broad range of research, from rapid evaluative studies to foundational interviews, surveys, diary studies, journey research, and mixed\-methods programs
- Partner with senior cross\-functional stakeholders to inform product strategy, evaluate concepts and prototypes, and improve live customer experiences
- Define research plans for ambiguous problem spaces and use strong methodological judgment to select the right approach for the decision, product stage, and level of risk
- Help teams reduce ambiguity and de\-risk investments by identifying unmet customer needs, product gaps, adoption barriers, and trust issues early
- Translate research findings into clear recommendations that improve usability, adoption, trust, and overall customer experience quality
- Influence roadmaps and prioritization by ensuring customer evidence is visible, credible, and actionable at the right moments
- Build deep domain expertise through ongoing study of customer workflows, prototypes, competitive products, and industry trends
- Mentor junior researchers and contribute to raising the quality, consistency, and impact of research across the team
- Explore thoughtful ways to use AI in the research and product development process where it improves speed, depth, or quality
About the team
The AWS Applied AI Solutions team is at the forefront of AI innovation, designing and building the next generation of intelligent business applications. We work closely with global Product, Engineering, Science, Marketing, and Business Development teams to deliver compelling AI\-powered experiences that transform how businesses operate. Our research team plays a critical role in ensuring these AI solutions are not just technically advanced, but truly meet customer needs and drive meaningful business outcomes.BASIC QUALIFICATIONS
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- 5\+ years of proven success leading User Research projects with demonstrated impact experience
- 5\+ years of hands\-on work with: field research, ethnography, lab\-based user testing, remote testing, paper prototype testing, iterative prototype testing, concept testing, and survey design experience
- Bachelor's degree in HCDE, Human Factors, Cognitive Psychology, or a related field
- Experience in leading User Research projects with demonstrated impact
- Experience with field research, ethnography, lab\-based user testing, remote testing, paper prototype testing, iterative prototype testing, concept testing, and survey design
- Experience with all aspects of research (study design, recruiting, moderation, analysis, reporting)
- Experience with behavioral data collection, quantitative data analysis, and statistics
- Have a portfolio demonstrating past work experience and deliverables (e.g., study plans, reports, personas)
PREFERRED QUALIFICATIONS
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- Master's degree in Human Factors, HCDE, Cognitive Psychology or equivalent
- Experience in a technical field (software development, network development, IT, other related)
- Experience with UX Research in non\-US markets
- Experience working in a highly Agile/Scrum environment
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, Sunnyvale \- 167,400\.00 \- 226,500\.00 USD annually
USA, NY, New York \- 167,400\.00 \- 226,500\.00 USD annually
USA, WA, Seattle \- 152,200\.00 \- 205,900\.00 USD annually
Salary Context
This $167K-$226K 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($196K) sits 10% below the category median. Disclosed range: $167K to $226K.
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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