Applied Scientist, Customer360

Amazon is hiring an Applied Scientist, Customer360 in Seattle, United States. It pays $143k-$193k a year and Level rates it ; you can apply on Level.

AI in this role

fine-tuning

Amazon serves hundreds of millions of customers. Each one has a unique history of purchases, preferences, and behaviors. Our team's mission: turn that history into real-time contextual intelligence that makes every Amazon experience feel personal.

We're hiring an Applied Scientist to push the boundaries of what's possible with LLMs, semantic retrieval, and customer understanding at scale.

The problem space: Imagine a system that can instantly synthesize years of customer signals — what they bought, what they love, what they're planning — and surface the exact right context for any experience, in milliseconds. That's what we build. It's equal parts information retrieval, generative AI, and systems engineering.

Why this role:

1. Scale: Your models will serve 1,500+ requests per second across Amazon's largest surfaces.
2. Impact: Direct revenue attribution in the hundreds of millions — your work shows up in customer experiences the same week.
3. Frontier tech: Fine-tuning LLMs, building custom embedding models, designing retrieval architectures that balance quality with sub-100ms latency constraints.
4. Data richness: Access to one of the most comprehensive customer behavior datasets anywhere.
5. Ownership: End-to-end — from research to production deployment to metric evaluation.

Key job responsibilities
1. Invent new approaches to contextual retrieval, relevance scoring, and LLM-based summarization.
2. Fine-tune and evaluate language models for domain-specific understanding.
3. Design experiments that measure real customer impact, not just benchmark scores.
4. Ship production systems and iterate based on live metrics.
5. Collaborate across teams — Alexa, Search, Recommendations — as a platform that powers them all.
6. Mentor team members and shape the technical direction of our roadmap.

Please visit https://www.amazon.science for more information.

A day in the life
You'll analyze large-scale behavioral data, design experiments, and build models that ship to production. You'll work closely with engineers to ensure your science translates into low-latency, high-reliability systems. You'll present findings to leadership and influence product strategy. Some weeks you'll be deep in model architecture; other weeks you'll be debugging a relevance gap in production. Every day, your work reaches real customers.

About the team
We're a small, high-impact team that values scientific rigor and engineering craft equally. The team values innovations and offers a safe place to try, fail and learn while fostering a culture of continuous improvement. Everyone is a leader and owner for everything we do as a team. Our team offers creative space with entrepreneurial work environment focusing on customer obsession.

Basic qualifications

- 3+ years of building models for business application experience
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
- Experience in designing experiments and statistical analysis of results
- Experience in patents or publications at top-tier peer-reviewed conferences or journals

Preferred qualifications

- Experience using Unix/Linux
- Experience in professional software development

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, WA, Seattle - 142,800.00 - 193,200.00 USD annually

How we rate this

Applied Scientist, Customer360 at Amazon rates 93 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Key facts

Applied Scientist, Customer360 is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $143k-$193k a year.
The role is based in US, WA, Seattle.
The job was posted on October 15, 2026.
Seniority: mid.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Fine-tuning

Also common in applied scientist jobs at this level

Machine learning Deep learning NLP TensorFlow Spark Reinforcement learning

Pay

This job starts at: $143k
Typical applied scientist job: $167k

This posting starts 15% below the typical applied scientist job. See applied scientist jobs

Questions you could be asked

Walk me through fine-tuning a model: what data did you use, and how did you check the result?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: Fine-tuning. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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