Applied Scientist, FinTelligence

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

Level's read on this job

Level rates this applied scientist role Builds AI ●●●●: the job is building AI systems.
The posting mentions Python, Java and C among its tools and skills.
Level classes it as a senior role.
Amazon has 20,000+ other open roles on Level.

AI in this role

Lead development of generative AI applications and multi-agent financial systems processing millions of transactions.

PythonJavaCLarge Language Models

ai-agentsnlpspeechmachine-learningapplied-sciencegenerative-aimulti-agent-systems

At Amazon's FinTech organization, we are building AI systems that process hundreds of millions of financial transactions, turn complex documents into actionable intelligence, and power autonomous agents that learn from every customer interaction. We are looking for an Applied Scientist to lead the development of generative AI applications that change how finance teams work, tackling problems at the intersection of large language models, multi-agent systems, and real-world financial operations.

Key job responsibilities
- Building AI systems that finance teams trust enough to rely on without manual review, where precision isn't a nice-to-have, it's a compliance requirement.
- Designing agents that learn from user corrections and get measurably better with every interaction, not just at the next model release.
- Solving inference at massive scale using tiered model architectures, intelligent routing, and small language models that deliver production-grade accuracy at a fraction of frontier model cost.
- Developing evaluation frameworks that catch quality regressions before customers do and gate every model change before it ships.

Who Thrives Here
- You're someone who cares as much about shipping as about research.
- You've built models that run in production, not just in notebooks.
- You're comfortable working across the full stack, from model architecture to deployment to measuring whether the customer's workflow actually changed.
- You operate well in cross-functional settings where science, engineering, and business teams inform each other continuously.
- You'd rather solve a hard real-world problem than optimize a benchmark.

What Makes This Different
- Your work ships to production and directly changes how thousands of finance professionals operate daily.
- The problems are genuinely hard: financial data is messy, regulated, high-stakes, and operates at a scale where naive LLM approaches break down.
- You'll work across multiple domains, from contract intelligence to cash application to financial data investigation, not a single narrow use case.

Basic qualifications

- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience building machine learning models or developing algorithms for business application
- Experience programming in Java, C++, Python or related language
- 3+ years of building models for business application experience

Preferred qualifications

- PhD in computer science, machine learning, engineering, or related fields
- Experience in building speech recognition, machine translation and natural language processing systems (e.g., commercial speech products or government speech projects)
- Experience in patents or publications at top-tier peer-reviewed conferences or journals

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, Bellevue - 142,800.00 - 193,200.00 USD annually

How we rate this

Applied Scientist, FinTelligence at Amazon rates 90 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. Read the description and count the AI tools, models and workflows it names.

Key facts

Applied Scientist, FinTelligence 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, Bellevue.
The job was posted on June 15, 2026.
Seniority: senior.
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

AI agentsNLPSpeechMachine learningApplied ScienceGenerative AIMulti Agent SystemsPython

Also common in applied scientist jobs at this level

Deep learning Reinforcement learning TensorFlow Spark AWS Computer vision

Pay

This job starts at: $143k
Typical applied scientist job: $172k

This posting starts 17% below the typical applied scientist job. See applied scientist jobs

Questions you could be asked

How do you decide when an AI agent can act on its own versus asking for approval first?
What NLP problem have you worked on, and how did you measure whether it actually worked?
What have you built with speech recognition or text-to-speech, and where did it break?
Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where applied science was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: AI agents, NLP, Speech, Machine learning, and Applied Science. 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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