AmazonPosted 2w ago

Applied Scientist-II, Amazon Music - Search Science, Amazon Music – Search Science

Applied Scientist-II, Amazon Music - Search Science, Amazon Music – Search Science at Amazon scores 100 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

IN, KA, Bengalurumidfull-time

AI in this role

fine-tuningnlp

Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture.

The Amazon Music Search Science team is seeking an innovative and driven Applied Scientist to join our engineering and science hub in Bangalore. You will work alongside a world-class team of machine learning experts to break new ground in understanding user intent, classifying complex audio and musical forms, and creating next-generation interactive search experiences that help users find the exact music, podcasts, and audio content they are in the mood for.

In this role, you will own the design, development, and deployment of end-to-end machine learning systems. You will balance execution on core search and discovery priorities—such as improving retrieval accuracy, latency, and relevance for millions of daily queries—while laying the foundational modeling capabilities for broader semantic understanding and advanced conversational search experiences across mobile, web, and voice-forward devices (like Alexa and Echo).

Key job responsibilities
- Core Search & Execution: Collaborate with scientists, software engineers, and product managers to define, frame, and solve complex business and ranking problems as machine learning, information retrieval, or optimization tasks.
- Advanced AI & Modeling: Design, build, train, and evaluate production-grade ML models using classical machine learning, deep learning, Large Language Models (LLMs), and Agentic AI techniques to scale music discovery and intent resolution.
- End-to-End Production Ownership: Take algorithms from research ideation to production deployment. Build scalable data pipelines, efficient model-serving systems, and robust offline/online evaluation frameworks.
- Experimentation & Iteration: Design and analyze large-scale A/B experiments across millions of customers to measure impact on search relevance, engagement, and customer satisfaction, refining models for continuous improvement.
- Forward-Looking Innovation: Research and implement novel statistical and machine learning approaches, exploring multi-modal understanding, rich content semantics, and advanced retrieval mechanisms that extend beyond traditional search boundaries.
- Technical Communication: Communicate findings, architectural decisions, and technical roadmaps clearly to both technical peers and executive stakeholders, authoring robust design documents and contributing to team standards.

Basic Qualifications
- PhD, or Master’s degree and 4+ years of relevant experience in Computer Science, Computer Engineering, Machine Learning, Statistics, or a related quantitative field.
- 3+ years of hands-on experience building machine learning models or algorithms for business applications and deploying them into production.
- Strong programming skills in Python, Java, C++, or related languages, with a solid foundation in data structures, algorithms, and object-oriented design.
- Experience in one or more of the following areas: Information Retrieval, Natural Language Processing (NLP), Recommender Systems, Deep Learning, or Numerical Optimization.
- Demonstrated ability to work effectively with cross-functional teams in a fast-paced environment.

Preferred Qualifications
- Experience with large-scale distributed computing frameworks and big data systems (e.g., Spark, Hadoop, AWS infrastructure).
- Experience building search ranking, query understanding, or semantic retrieval systems for high-scale consumer applications.
- Familiarity with modern foundation models, LLMs, fine-tuning techniques, and efficient inference optimization for production services.
- Track record of peer-reviewed publications or patents at top-tier machine learning/AI conferences (e.g., NeurIPS, KDD, ACL, SIGIR, ICML).
- Experience in designing, executing, and evaluating rigorous online A/B experiments.

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

Preferred qualifications

- Experience using Unix/Linux
- PhD in computer science, machine learning, engineering, or related fields

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 TuningNlp

Questions you could be asked

Walk me through fine-tuning a model: what data did you use, and how did you check the result?
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?

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List these exact terms on your resume: Fine Tuning and 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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