Amazon Interview 2026: Process, Behavioral Questions & Bar Raiser
By
Ethan Fahey
•
Feb 13, 2026
If you’re a senior AI engineer with experience building LLM serving infrastructure, you can probably resonate with this. Your inbox is full: an Amazon recruiter reaches out about an Applied Scientist role, a startup founder messages you on LinkedIn, and you’ve got a Fonzi Match Day coming up. You’re juggling online assessments, reviewing STAR-method stories for an upcoming Loop, and keeping Amazon’s Leadership Principles straight. This kind of overlap is now common as demand for experienced AI, ML, and infra engineers continues to outpace supply.
Amazon remains a major draw because of its scale, with AWS Bedrock, Alexa, Ads, and robotics all pushing AI into real production, but that scale comes with a deliberately tough interview process. In 2026, that means remote Loops, stricter limits on AI tools during assessments, and highly structured behavioral interviews alongside deep technical evaluation. This guide breaks down what Amazon interviews look like today, how to prepare effectively, and how Fonzi AI helps engineers stay in control of their search.
Key Takeaways
Long, rigorous funnel: Amazon’s 2026 interview process typically takes 4–8 weeks and includes online assessments, recruiter screens, technical phone interviews, and the half-day “Loop” with 4–6 back-to-back interviews.
Balanced but demanding interviews: AI, ML, infra, and LLM candidates face roughly a 50/50 split between coding + system design and behavioral questions tied tightly to Amazon’s Leadership Principles, with STAR-format answers expected
Bar raiser veto power: A bar raiser from outside the team can block a hire regardless of other feedback, contributing to an acceptance rate under 1% for top roles and post-Loop response times of 1–4 weeks.
Parallel options matter: While preparing for Amazon, many candidates use Fonzi AI as a complementary channel; its curated, bias-audited Match Day events connect AI and infra engineers with multiple high-growth companies and often lead to offers within 48 hours.
Human-centered AI hiring: At Fonzi, AI handles logistics and fraud detection, reducing noise and bias so interviewers and candidates can focus on high-quality, human judgment where it matters most.
Amazon Interview Stages in 2026
Amazon’s interview process is decentralized by team: Alexa, AWS AI, Amazon Ads, robotics, and others each have their own hiring managers and specific requirements. But for most technical roles, the structure follows a predictable pattern that experienced candidates can prepare for systematically.
The typical stages unfold in this order:
Application or referral: You enter the funnel via Amazon.jobs, LinkedIn, internal referral, or outbound recruiter sourcing
Recruiter screen: A 30-45 minute phone call covering your background, location, compensation, and initial leadership principle alignment
Online assessment(s): Coding challenges, work simulations, and sometimes ML-specific problems delivered via platforms like HackerRank
Technical phone or virtual screens: One or two 45-60 minute sessions mixing coding with behavioral interview questions
On-site or virtual Loop: The main event: 4-6 back-to-back interviews over half a day
Debrief and offer: The hiring team meets, the bar raiser weighs in, and you receive a decision within 3-7 business days (sometimes longer)
Many candidates don’t realize they can be under consideration for multiple teams simultaneously, especially within AWS and Amazon Devices. If one team passes, your recruiter may route you to another without restarting the entire process.
AI coding assistants like ChatGPT, GitHub Copilot, and similar tools are explicitly prohibited during online assessments and live coding rounds. Amazon monitors for cheating and takes violations seriously; permanent blacklisting is a real risk. The expectation is that your problem-solving skills are genuinely yours.
Stage 1: Application & Recruiter Screen for AI/ML and Infra Roles
For senior AI engineers, ML researchers, and infra engineers, entry into Amazon’s pipeline typically happens through one of three channels: internal referrals from current Amazonians, outbound sourcing by recruiters who find your profile on LinkedIn or at conferences, or direct applications through Amazon.jobs. Referrals move faster and signal stronger initial interest, but all paths lead to the same interview loop eventually.
Recruiters scanning resumes for AI and infrastructure roles look for specific signals: quantified impact statements (“reduced inference latency by 40%,” “trained a 70B-parameter model on 1.2T tokens”), clear ownership of systems rather than participation in team projects, and familiarity with AWS services, distributed training frameworks, and LLM tooling. Vague descriptions of responsibilities don’t cut it; hiring managers want to see personal contributions with measurable outcomes.
The initial recruiter call, typically scheduled within 1-2 weeks of application for strong profiles, runs 30-45 minutes. Expect questions about your career chronology, location flexibility, compensation expectations (Amazon will share target bands), and early probes into leadership principle alignment. Even at this stage, recruiters often ask behavioral interview questions like “Tell me about a time you went above and beyond for a customer” or “Describe a situation where you took ownership of a problem outside your job description.”
Prepare 2-3 concise star method stories before this call. Even for highly technical roles, your ability to articulate specific examples of customer obsession and ownership matters from the first conversation. Practice answering questions in a structured way: set context quickly, clarify your responsibility, describe your actions in detail, and quantify the result.
Fonzi AI simplifies this early filtering stage significantly. Profiles on the platform are pre-vetted by experienced recruiters, salary bands are disclosed by companies upfront, and many of the preliminary fit questions are resolved before any conversation happens. This means less time spent on logistics and more focus on substance.
Stage 2: Online Assessments & Technical Screens
Many Amazon AI, SDE, and data roles in 2026 still include an online assessment (OA), especially for candidates below staff level. These assessments are typically delivered via platforms similar to HackerRank or Codility and must be completed within 5-7 days of receiving the invitation.
For AI/ML and infra candidates, typical OA content includes:
Data structures and algorithms (graphs, trees, arrays, hashing, heaps)
Basic to intermediate coding challenges (often LeetCode medium difficulty, like finding the Kth largest element)
Debugging exercises or math-heavy ML questions
Work simulation scenarios testing judgment under ambiguity
OA elimination rates are high, with 70-80% of applicants being filtered out at this stage. The assessment isn’t just testing whether you can solve problems; it’s testing whether you can solve them quickly, correctly, and with clean code under time pressure.
For senior and principal-level candidates, Amazon may substitute or supplement the standardized OA with one or two 60-minute virtual technical screens. These sessions combine hands-on coding (expect to share your screen and work in a collaborative editor) with high-level system or ML design discussions. Interviewers want to see how you think through a complex problem, not just whether you arrive at the right answer.
Generative AI tools are strictly prohibited during all assessments and screens. Amazon monitors for anomalous patterns and takes violations seriously. Candidates caught using external AI assistance can be permanently blacklisted from future consideration. The job requires genuine technical skills; there’s no way to fake your way through.
Preparation strategies that work:
Solve 2-3 medium-level DSA problems daily under timed conditions
Implement core ML algorithms from scratch (gradient descent, decision trees, basic neural networks)
Practice whiteboarding distributed training or model serving architectures
Focus on algorithm questions involving graphs, dynamic programming, and heap operations
Stage 3: The Amazon Interview Loop & the Bar Raiser
The Loop is the centerpiece of Amazon’s process: a sequence of 4-6 back-to-back interviews, each lasting 45-60 minutes, typically conducted over half a day. Your panel includes engineers from the team, the hiring manager, possibly a product partner or stakeholder, and the bar raiser, a specially trained senior interviewer from outside the hiring team.
In 2026, most Loops will be conducted virtually using Amazon’s internal video tooling, with shared coding pads and whiteboard-style canvases for system design. Some candidates still visit offices in Seattle, NYC, Bangalore, or London, but virtual loops have become the norm and are treated with equal rigor. Expect short breaks between rounds, but plan for sustained cognitive load across several hours.
For AI SDE, applied scientist, and infra roles, the typical round mix looks like this:
Round Type | Typical Count | Focus Areas |
Coding | 1-2 | Algorithms, data structures, implementation quality |
System/ML Design | 1-2 | Scalability, latency, ML pipelines, evaluation frameworks |
Behavioral | 1-2 | Leadership Principles, STAR stories, conflict resolution |
Every technical round also includes 1-3 behavioral questions, so the line between “coding interview” and “behavioral interview” is blurry in practice.
The bar raiser is the most misunderstood element of Amazon’s process. They’re a specially trained senior interviewer, one of over 20,000 across Amazon by 2026, whose job is to ensure you meaningfully “raise the bar” for that level. They’re not from your target team, which gives them an objective interviewer perspective. They focus heavily on leadership principles rather than team-specific skills, and they hold veto power over the hiring decision.
Here’s the timeline to expect:
Loops are usually scheduled 1-3 weeks after passing technical screens
Decisions typically arrive 3-7 business days after the Loop
Complex or multi-team situations can extend this to 2-4 weeks
Bar Raiser veto can cause delays even when other interviews go well
Types of Amazon Technical Interviews: Coding, System Design & ML/LLM Design
Even in AI/ML-heavy roles, Amazon expects solid software fundamentals. The company’s success depends on building systems that scale, and hiring managers want engineers who can design and implement reliable infrastructure—not just train models in notebooks.
Coding Interviews
Coding rounds emphasize correctness, clarity, and complexity analysis. You’ll typically work in Python, Java, or C++ via a shared editor while an interviewer observes and asks clarifying questions. Here are some common Python interview questions. Expect medium-level problems solvable in 20-30 minutes, leaving time for discussion of edge cases, optimization, and follow-up questions.
Common topics include:
Arrays and strings manipulation
Tree and graph traversal
Dynamic programming
Heap and priority queue operations
Hash maps for efficient lookups
System Design Interviews
For senior engineers, system design rounds test your ability to architect scalable, fault-tolerant systems. You might design a recommendation engine, a logging pipeline for ML experiments, a distributed feature store, or a real-time bidding system for ads.
Interviewers evaluate:
Understanding of throughput, latency, and availability tradeoffs
Familiarity with AWS primitives (S3, DynamoDB, Kinesis, SageMaker)
Ability to make and defend design decisions under ambiguity
Consideration of failure modes and monitoring
ML/LLM Design Interviews
For science roles and applied scientists, ML design rounds focus on building production systems, not just training models. Topics include:
Recommender systems with A/B testing frameworks
Ranking pipelines with evaluation metrics
Generative AI services (chatbots, content generation on AWS Bedrock)
Data quality, feature engineering, and model deployment
LLM-specific challenges: MoE architectures, parallelism strategies, quality validation pipelines
Remember: every technical question round includes behavioral components. Be ready to context-switch between coding, architecture diagrams, and star method stories, sometimes within the same interview.
Amazon Behavioral Interviews & Leadership Principles
Behavioral performance can override borderline technical performance, especially for mid-senior, senior, and principal roles. Amazon relies on behavioral signals to predict how you’ll operate in their high-ownership, customer-obsessed culture. Technical skills get you in the door; leadership principles determine if you stay.
The 16 leadership principles aren’t lip service; they’re the actual rubric interviewers use to evaluate candidates. For engineering roles, the most commonly probed principles include:
Principle | What Interviewers Look For |
Customer Obsession | Decisions driven by customer experience, not internal convenience |
Ownership | Taking responsibility beyond your job description |
Dive Deep | Knowing details several levels down, not just surface understanding |
Deliver Results | Shipping on time with measurable outcomes |
Bias for Action | Moving quickly under uncertainty, reversible decisions |
Are Right, A Lot | Good judgment, willingness to update beliefs with data |
Invent and Simplify | Finding innovative solutions that reduce complexity |
Amazon interviewers are trained to dig deep with follow-up questions. Expect probes like “What exactly did you do versus the team?”, “What did you learn from that failure?”, and “What would you do differently now?” Vague answers that rely on “we” instead of “I” will get flagged. They want specific examples with quantified impact.
Example behavioral questions for AI/ML work:
“Tell me about a time you shipped an ML model that initially hurt a metric. How did you respond?”
“Describe a situation where you had to push back on a launch due to model bias concerns.”
“Give an example of diving deep into a production issue that others had given up on.”
“Tell me about a time you simplified a complex ML pipeline. What was the impact?”
Behavioral questions appear at every stage, from the recruiter screen to the bar raiser round. Build a story bank of 10-15 specific examples tagged by principle, and practice adapting them on the fly.
How the Bar Raiser Evaluates You
You won’t be told which interviewer is the bar raiser. But they’re often from a different team, they have a neutral agenda (not selling you on the role), and they focus heavily on leadership principles rather than team-specific technical question details.
The bar raiser’s job is to judge whether you’re stronger than at least 50% of current Amazonians at your target level. They’re evaluating:
Long-term impact potential
Judgment under ambiguity
Cultural alignment with Amazon’s culture
Ability to raise standards for team members around you
Bar Raisers probe multiple deep STAR stories, often revisiting earlier answers for consistency. They ask “what if” variations to test your reasoning, explore conflict and failure scenarios, and pay attention to how you discuss collaboration with others. They’re specifically looking for evidence that you take ownership, learn from mistakes, and operate with customer satisfaction in mind.
How to treat the Bar Raiser round
Assume every interviewer could be the bar raiser. Answer precisely with specific examples, quantify results whenever possible, acknowledge mistakes openly, and highlight scaling impact and lessons learned. Defensiveness or vagueness are red flags.
In debriefs, the bar raiser weighs all feedback from other interviews and can veto a hire even when the hiring manager is enthusiastic. Their role exists specifically to prevent “good enough” hires. Amazon constantly strives to raise the bar, and the bar raiser enforces that standard across 40-50% of otherwise positive candidates.
Using the STAR Method for Amazon Behavioral Questions
The star method (Situation, Task, Action, Result) isn’t just a recommendation at Amazon; it’s effectively mandatory. The company uses it because it standardizes evaluation, making it easier for interviewers to write consistent feedback and for panels to calibrate across candidates.
Here’s the framework:
Situation: Set context briefly (when, where, what was happening)
Task: Clarify your specific responsibility
Action: Describe exactly what you did (this should be the longest part)
Result: Quantify the outcome and include any lessons learned
Example STAR answer for an AI/ML scenario
Situation: “In Q3 2024, our recommendation model’s latency had crept up to 200ms P99, causing customer experience degradation on mobile.”
Task: “As the tech lead for the serving infrastructure, I was responsible for getting latency back under our 100ms SLA without sacrificing model quality.”
Action: “I analyzed the serving pipeline, identified that we were loading redundant embeddings on each request, and designed a caching layer that precomputed and stored hot embeddings in Redis. I also worked with the ML team to prune model parameters by 15% using knowledge distillation, which reduced inference cost without accuracy impact.”
Result: “We reduced P99 latency to 85ms, a 57% improvement, and saved $120K annually in serving costs. The approach became our standard template for all new models.”
Best practices for STAR answers
Keep Situation and Task to 20% of your answer
Allocate 50-60% to Action, emphasizing your personal contributions
Always include numbers in Result (latency, cost, revenue, accuracy, retention)
Add a brief reflection on what you learned or would do differently
Tag each story to at least two leadership principles for flexibility
STAR shouldn’t sound robotic. Aim for a conversational tone with disciplined structure. You’re having a conversation about your experience, not reciting a script. But the structure keeps you on track during 45-minute interviews where wandering answers waste precious time.
Responsible AI in Hiring: How Amazon Thinks vs. How Fonzi AI Works
Candidates in 2026 have legitimate concerns about AI in hiring. Resume screening tools, automated ranking systems, and opaque scoring mechanisms can feel like being reduced to a “profile score” rather than being evaluated as a person. Understanding how different aspects of AI are used, and where human judgment remains central, matters.
Large employers like Amazon use AI for candidate funnel analytics, resume parsing and keyword matching, job matching suggestions, and scheduling optimization. But the core evaluation still happens through human interviewers and the bar raiser mechanism. The final hiring decision is made by people in a debrief meeting, not an algorithm.
Fonzi AI takes a different approach. We use AI to remove noise, such as fraud detection, duplicate profile identification, and logistics coordination, and reduce bias through audited evaluation rubrics. But we don’t auto-reject strong engineers based on opaque scores or pedigree assumptions.
Key differences in Fonzi’s philosophy:
Evaluations are bias-audited with structured criteria for ML/infra/LLM roles
Engineers are assessed on skills and demonstrated impact, not school names or accent
AI handles administrative tasks so recruiters can spend time on deep, human conversations
Salary transparency is built into the process from the start
Communication skills and cultural fit are evaluated by experienced humans, not algorithms
AI in hiring, when designed responsibly, frees recruiters to focus on what matters: understanding your career trajectory, reviewing your portfolio, asking thoughtful questions, and providing genuine feedback. The goal is better human decisions, not replacing human judgment.
Amazon Interview Loop vs. Fonzi AI Match Day
For AI engineers deciding how to allocate their interview time and energy, understanding the tradeoffs between different paths matters. Here’s how Amazon’s traditional Loop compares to Fonzi AI’s Match Day experience:
Aspect | Amazon Interview Loop (2026) | Fonzi AI Match Day |
Total Timeline | 4-8 weeks from application to offer | 1-2 weeks from profile to Match Day; offers within 48 hours of event |
Number of Interviews | 6-10 total (screens + 4-6 Loop rounds) | 3-6 focused interviews across multiple companies in one event |
Salary Transparency | Bands shared after recruiter screen | Disclosed upfront before any interviews |
Use of AI | Resume parsing, scheduling; human evaluation | Bias-audited matching, fraud detection; human evaluation |
Bias Controls | Bar Raiser consistency mechanism | Structured rubrics, audited criteria, diverse panels |
Decision Time Frame | 3-7 business days post-Loop (up to 4 weeks) | Typically within 48 hours of Match Day |
Parallel Optionality | Single company focus per process | Multiple companies in one coordinated event |
Recruiter Support | Assigned Amazon recruiter | Dedicated Fonzi concierge recruiter |
You don’t have to choose one or the other. Many candidates run Amazon’s process in parallel with Fonzi Match Days. The competing offers provide negotiating leverage, the additional interview practice sharpens your skills, and having options reduces the stress of any single decision. Your interview preparation for Amazon transfers directly to Match Day companies; the same systems thinking, storytelling discipline, and ML rigor apply across top AI companies.
How Fonzi AI’s Match Day Helps AI Engineers Navigate Amazon-Level Processes
Match Day is a structured, time-boxed hiring event where pre-vetted AI/ML, infra, and data engineers meet several high-growth AI startups and tech companies at once. Instead of serial, weeks-long processes with individual companies, you batch your interviews into a focused 24-48-hour window.
The candidate experience works like this:
Create a single Fonzi profile highlighting your experience, technical focus areas, and salary expectations
Get matched to companies building in areas you care about (LLMs, distributed systems, ML infrastructure)
Receive curated introductions to teams that align with your skills
Complete all first-round interviews in one coordinated event
Receive offers (typically multiple) within 48 hours of the event
This contrasts sharply with the drawn-out, serial nature of big-tech processes. Instead of waiting 4-8 weeks for one Amazon decision, you can generate multiple competing offers that strengthen your negotiating position, whether you ultimately choose Amazon or not.
Specific Fonzi features that help:
Concierge recruiter support throughout the process
Pre-briefs on each company’s tech stack and interview style
Scheduling is handled entirely by the platform
Post-interview feedback loops so you know where you stand
Salary transparency from day one, no guessing games
Consider an experienced LLM infrastructure engineer who applies to Amazon in early January, schedules their Loop for mid-February, and joins a Fonzi Match Day in late January. By the time their Amazon debrief happens, they already have two startup job offers in hand. This creates optionality and leverage that transform the negotiation dynamic.
Fonzi’s process runs in parallel with Amazon, so you don’t have to pause or abandon your big-tech pipeline to benefit from additional options.
Practical Preparation Tips for Amazon Interviews (AI/ML & Infra Focus)
Whether you’re targeting Amazon, joining a Match Day, or running both processes simultaneously, this checklist will help you prepare for interviews at the highest levels.
Technical Prep
Daily coding practice: Solve 2-3 medium-level problems on LeetCode or similar platforms, focusing on graphs, trees, dynamic programming, and heap operations
Fundamentals review: Revisit operating systems (memory management, process scheduling), networking (TCP/IP, load balancing), and concurrency patterns
ML system design: Practice designing feature stores, model registries, training pipelines, and serving infrastructure on a whiteboard or shared canvas
LLM-specific depth: Be ready to discuss Transformer architectures, MoE models, parallelism strategies, and quality validation pipelines for generative AI
Behavioral Prep
Build your story bank: Write out 10-15 STAR stories covering ownership, conflict, failure, customer impact, and technical innovation
Rehearse out loud: Practice answering behavioral interview questions verbally. Written prep is necessary but insufficient
Record mock interviews: Review recordings to identify filler words, vague language, and areas where you drift from the STAR structure
Map stories to principles: Each story should connect to at least two of Amazon’s leadership principles for flexibility during interviews
Logistics Tactics
Confirm your environment: Test internet stability, IDE setup, and whiteboard tools before any virtual interview
Clarify time zones: Double-check scheduling details, especially for international candidates
Plan breaks: During Loop days, use short breaks between rounds to reset mentally, hydrate, stretch, and review notes
Prepare mock interviews with realistic conditions: 45-minute AI interview practice sessions with follow-up questions, not just problem-solving in isolation
Match Day and Fonzi recruiters can help refine resumes, identify career narratives, and run mock interviews tailored to Amazon-style expectations. The same interview tips apply across top-tier companies; preparation is preparation.
Conclusion
Amazon is still a high-bar, high-signal destination for AI/ML and infrastructure engineers in 2026. The interview loop is intense by design, often 4–8 weeks long, with deep dives into Leadership Principles, hands-on technical rounds that test both coding and system design, and a bar raiser who can veto a hire to keep standards high. It’s not the right fit for everyone, but for engineers who prepare intentionally, Amazon’s scale and impact can be career-defining.
The good news is that candidates have more leverage than ever. You can prepare methodically with frameworks like STAR, use responsible AI tools to practice and get feedback, and run multiple interview processes in parallel instead of waiting on one outcome. That’s where Fonzi AI fits in: we use AI to streamline logistics, reduce bias, and surface high-signal matches, while keeping humans at the center of evaluation. By joining a Fonzi Match Day, you can interview with multiple AI-first companies in a tight window, build leverage alongside an Amazon process, and turn your preparation into real options across the 2026 AI job market.
