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AI Benchmarking Specialist, SFIX

Amazon

AI Benchmarking Specialist, SFIX

Amazon

Bangalore, KA, IND

·

On-site

·

Full-time

·

1mo ago

Benefits & Perks

Flexible PTO policy

Annual team offsites

Wellness benefits

Learning and development stipend

Required Skills

Airflow

Apache Spark

SQL

The Seller AI team within International Seller Services organization is focused on helping sellers with the right set of Gen-AI/LLM powered tools and agentic solutions that can enable them to accelerate business growth on Amazon. Our primary focus lies in handling annotations for training, measuring, and improving Artificial Intelligence (AI) and Large Language Models (LLMs), enabling Amazon to deliver a superior seller experience to our sellers worldwide. The AI Benchmarking Associate supports the evaluation of AI systems by designing and executing benchmarking and audit activities to assess model quality, compliance, robustness, and fairness. The role combines elements of AI auditing, quality assurance, and traditional audit-style documentation and stakeholder communication. By joining us, you will play a pivotal role in shaping the future of selling on Amazon for sellers worldwide.
Key job responsibilities
Key job responsibilities

  • As part of your role, you will have the opportunity to,
  • Assist in planning and executing benchmarking exercises for AI models, including defining test plans, metrics, and acceptance criteria across accuracy, robustness, bias, and reliability
  • Support content accuracy, relevancy, and privacy checks by reviewing datasets, model outputs, and data handling practices, escalating potential regulatory risks.
  • Validate data based on specific annotation guidelines, ensuring the accuracy and quality of the collected information
  • Prepare clear audit and benchmarking reports, including error ratings, root-cause analysis, and recommendations, and contribute to presentations for senior stakeholders
  • Maintain organized audit documentation, evidence, and benchmarking datasets to support internal review
  • You will work closely with your team members and managers to drive process efficiencies and explore opportunities for automation
  • You will strive to enhance the productivity and effectiveness of the data generation by contributing to the development and continuous improvement of AI audit methodologies, checklists, and test frameworks as regulations and best practices evolve
    We are open to hiring candidates to work out of the following location:
    Bengaluru, Karnataka, IND

Basic Qualifications

  • Bachelor's degree or equivalent

Preferred Qualifications

  • 2+ years of equivalent experience
    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.

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About Amazon

Amazon

Amazon

Public

Amazon.com, Inc. is an American multinational technology company engaged in e-commerce, cloud computing, online advertising, digital streaming, and artificial intelligence.

10,001+

Employees

Seattle

Headquarters

Reviews

2.9

10 reviews

Work Life Balance

2.8

Compensation

3.7

Culture

2.5

Career

2.3

Management

2.1

35%

Recommend to a Friend

Pros

Good pay and compensation

Strong benefits package

Flexible scheduling options

Cons

Poor management and leadership

Limited growth and promotion opportunities

High stress and demanding work environment

Salary Ranges

2 data points

L2

L3

L4

L5

L6

L2 · Data Analyst L2

0 reports

$108,330

total / year

Base

$43,332

Stock

$54,165

Bonus

$10,833

$75,831

$140,829

Interview Experience

10 interviews

Difficulty

3.7

/ 5

Duration

21-35 weeks

Offer Rate

20%

Experience

Positive 10%

Neutral 10%

Negative 80%

Interview Process

1

Application Review

2

Recruiter Screen

3

Online Assessment

4

Technical Phone Screen

5

Onsite/Virtual Loop

6

Team Matching

7

Offer

Common Questions

Coding/Algorithm

System Design

Behavioral/STAR

Leadership Principles

Technical Knowledge