AmazonPosted 1mo ago

Machine Learning Data Associate, Journey Management

Machine Learning Data Associate, Journey Management at Amazon scores 16 out of 100 on AI centrality, which makes it AI Level 1 of 4 (Little AI) on this board. The level measures how much of the work is AI, not seniority.

IN, KA, Bengalurujuniorfull-time

AI in this role

fine-tuningai-data-labelingnlp

Machine Learning Data Associates are annotation specialists who support machine learning projects through high-quality data annotation, labeling, and analysis. In this role, you will help improve customer experience and the performance of ML models by producing accurate, reliable data that trains and evaluates these systems. You will work under the guidance of an operations team of subject matter experts, contributing to a wide range of projects that directly shape how customers interact with automated support.

This role is ideal for detail-oriented individuals who enjoy structured, guideline-driven work and want to play a direct part in advancing conversational AI and customer service technology.

Key job responsibilities
Data Annotation and Labeling

- Perform accurate annotation and labeling tasks that support model training and fine-tuning.

- Complete intent and dialogue labeling for language understanding and intent detection systems.

- Conduct multi-turn free-text annotation to support conversational AI experiences.

- Author simulated conversations used for testing and training.

Quality Assurance and Testing

- Test customer service models based on specific prompts to confirm intent detection and routing work as intended.

- Read and analyze customer contacts to identify defects and improvement opportunities.

- Audit question-and-answer pairs across multiple marketplaces for policy compliance and accuracy.

- Compare call audio to written transcripts to evaluate and improve transcription accuracy.
Analysis and Evaluation

- Complete customer experience analysis by reading contacts and assessing customer sentiment.

- Identify opportunities for improvement through structured contact review.

- Flag compliance issues, including exposure of personally identifiable information and policy violations.

- Review the quality of response templates used by automated customer service agents.
Performance and Development

- Maintain high quality standards across all assigned projects.

- Track and meet throughput targets and key performance indicators.

- Collaborate with project leads and the wider operations team.

- Participate in upskilling initiatives to build expertise across a variety of project types.

A day in the life
You start your day by reviewing your assigned projects and the guidelines for each. You might start with labeling customer conversations to help train a model to better understand what customers are asking for, then move into auditing question-and-answer pairs to make sure the answers customers receive are accurate and compliant with policy. Next, you could test a customer service model feature against a set of prompts, flag a transcription mismatch you noticed, and log your throughput for the day.

Throughout the day you work closely with subject matter expert project leads who answer questions, share feedback, and help you grow. Your work is measured with clear metrics, so you always know how you are performing and where you can improve.

About the team
We own the human-assisted work that produces and validates the data our machine learning models are trained and launched on. Partner teams bring us a model or customer experience, and we deliver the labeled data, translations, and findings they need to make confident launch decisions. Our work spans data annotation, localization and translation, translation quality assurance, end-to-end testing of chat, voice, and self-service experiences, and contact reading, where we analyze real customer interactions to flag defects, assess sentiment, and surface improvement opportunities. We partner closely with program / product managers and science teams

Basic qualifications

- Speak, write, and read fluently in English
- Bachelor's degree or equivalent
- Experience working with customers with a passion for delivering exceptional service, or experience that includes strong analytical skills, attention to detail, and effective communication abilities
- Can work proactively and independently, meet deadlines, and deliver on projects and tasks

Preferred qualifications

- Experience in natural language data labeling, data annotation, linguistic annotation or other forms of data markup
- Experience working with speech and text language data in multiple languages
- Experience using customer insights and data to deeply understand target customers and dive deep
- Experience prioritizing and handling multiple assignments at any given time while maintaining commitment to deadlines, or experience completing complex tasks quickly with little to no guidance and react with appropriate urgency to situations that require a quick turnaround
- Experience in machine learning, data mining, information retrieval, statistics or natural language processing, or experience in computer architecture
- Familiarity with evaluating conversational or automated customer service experiences.

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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Skills and AI tools this role asks for

Fine TuningAI Data LabelingNlp

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 do you keep labeling instructions consistent across a large annotation team?
What NLP problem have you worked on, and how did you measure whether it actually worked?

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List these exact terms on your resume: Fine Tuning, AI Data Labeling, and Nlp. An applicant tracking system matches the wording, not the idea.
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