Applied Scientist II, Sheriff Team- Payroll tech

Amazon is hiring an Applied Scientist II, Sheriff Team- Payroll tech in Hyderabad, India. Level rates it ; you can apply on Amazon.

Level's read on this job

Builds AI ●●●● is how Level rates this applied scientist role: the job is building AI systems.
Tools and skills the posting cites include NLP.
Level classifies it as a mid-level role.
Level lists 20,000+ other open roles at Amazon.

AI in this role

nlp

Payroll Tech's Sheriff team develops and maintains ML and Generative AI applications that support Payroll Operations and Amazon employees at scale. Our portfolio includes Pay-Input Anomaly Detection, which improves the pay experience by identifying pay input irregularities such as leaves and insurance discrepancies; Percept, which improves ticket resolution by providing intelligent ticket prioritization via sentiment scoring, ticket summarization, defect classification, and categorization; Penny, a Virtual Assistant that enables payroll operations teams to efficiently retrieve information from multiple sources including policies, Percept data, vendor data, and HR data via Xylem through a single browser interface; Pay Ticket Genie, in process of being integrated with Amazon AZA(A to Z Assistant); Niyam, our rule engine; and Policy as Code Extraction (PoCo), a critical component of SPACE (Single Payroll Autonomous and Computation Engine) Amazon's in-house payroll system built to eliminate third-party vendor dependency for payroll processing. PoCo ensures data accuracy by validating that pay instructions are correct and performing calculations when required. It consists of two components: policy-based rule creation, where business owners select a pay code and provide policy links to generate rules for specific business processes, and rule evaluation, where upstream services send real-time validation or calculation requests and receive results along with rationale for any failures. Sheriff team owns policy-based rule creation and powering the rule evaluation system with rules generated.
As an Applied Scientist on the Sheriff team, you will own and advance the ML and GenAI capabilities that power these systems: driving model accuracy, scientific innovation, and global scale across the payroll ecosystem.
Key Job Responsibilities
As an Applied Scientist on the Sheriff team, you will operate across three core dimensions: Invent, Implement, and Influence.
Invent
You bring deep domain knowledge and fluency with state-of-the-art scientific approaches as well as emerging technologies from the research community. You practice customer-obsessed science : working backwards from the needs of Amazon employees and payroll operations teams to extend or invent new ML approaches, even when no textbook solution exists. You design novel ML and LLM-based methodologies for anomaly detection, sentiment analysis, ticket classification, prescriptive analysis, intelligent virtual assistance, and automated policy extraction. You identify and define the research agenda for expanding Percept's capabilities including prescriptive analysis feature; lead the scientific strategy for the Penny-AZA integration enabling accurate and low-latency responses to Amazon employee payroll queries, and drive the ML strategy for Policy as Code extraction(PoCo), developing models that extract, interpret, and codify payroll policies into structured, executable rules that power real-time pay instruction validation and calculation within SPACE. You author or co-author articles for internal or external peer-reviewed venues that validate the novelty of your work, when appropriate and not precluded by business considerations.
Implement
The ML components you develop are directly integrated into production systems or directly support large-scale applications serving Amazon's global payroll operations. You make appropriate tradeoffs between model accuracy and latency, innovation and stability, and immediate versus long-term solutions; favoring reuse and established frameworks where appropriate. You make progress semi-autonomously with only occasional guidance, implement at the correct level of complexity the first time, and evaluate emerging technologies including Large Language Models (LLMs) and GenAI frameworks largely on your own. You ensure your models and pipelines integrate robustly with data sources including USC (Unified Central Service), Xylem, SIM-Ticketing, Pay Code Governance system, and PoCo's rule evaluation engine.
Influence
You contribute to tactical and strategic planning for the Sheriff team, including goals, priorities, and roadmaps for ML and GenAI capabilities. You lead the scientific strategy for the global expansion of Percept, driving both feature growth and country-level launches own the complex AI track for Penny-AZA integration collaborating across partner teams including Reflect and GREF to ensure seamless data integration and robust ML pipeline workflows, and drive the scientific roadmap for PoCo's expansion to 100K US employees, ensuring the ML models powering policy extraction, rule generation and testing scale reliably to meet this growth. You mentor scientists and engineers on the team and across teams, championing best practices for the AI-Driven Development Life Cycle (AIDLC) to rapidly increase developer productivity and delivery velocity. You provide peer feedback on research procedures and results within and across teams, help recruit and develop bar-raising talent through interview drives, and grow the team's organizational knowledge of Sheriff team ML solutions. You are visible in the broader internal and external scientific communities as a subject matter expert and regularly serve as a Program Committee (PC) member at peer-reviewed conferences or review articles for journal publications.

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 in patents or publications at top-tier peer-reviewed conferences or journals
- 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

Preferred qualifications

- Experience using Unix/Linux
- Experience in professional software development
- Experience communicating research findings and analysis in both written and spoken channels
- Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience leading and influencing your team or organization
- Experience in machine learning, data mining, information retrieval, statistics or natural language processing, or experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware
- Experience with training and deploying machine learning systems to solve large-scale optimizations, or experience with data infrastructures: relational analytic DBMS, Elastic-Search, and Big Data EMR/EC2/Glue/Lambda
- Experience using strong customer service, communication, and interpersonal skills

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.

How we rate this

Applied Scientist II, Sheriff Team- Payroll tech at Amazon rates 96 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 II, Sheriff Team- Payroll tech is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in IN, TS, Hyderabad.
The job was posted on May 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

NLP

Also common in applied scientist jobs at this level

Deep learning Reinforcement learning TensorFlow Spark AWS Computer vision

Pay

Typical applied scientist job: $169k

This posting doesn't list pay. Use the typical applied scientist figure when pay comes up. See applied scientist jobs

Questions you could be asked

What NLP problem have you worked on, and how did you measure whether it actually worked?
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: NLP. 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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