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

Amazon is hiring an Applied Scientist-II, Amazon Music - Search Science, Amazon Music – Search Science in Bengaluru, India. Level rates it ; you can apply on Amazon.

Level's read on this job

On Level's scale, this applied scientist role is Builds AI ●●●●: the job is building AI systems.
Tools and skills the posting cites include Alexa, Echo and fine tuning.
Level classifies it as a mid-level role.
Level lists 20,000+ other open roles at Amazon.

AI in this role

Design and deploy end-to-end machine learning models and conversational search experiences for Amazon Music.

AlexaEcho

fine-tuningnlpmachine-learningdeep-learningllmsinformation-retrievalpython

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.

How we rate this

Applied Scientist-II, Amazon Music - Search Science, Amazon Music – Search Science 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-II, Amazon Music - Search Science, Amazon Music – Search Science is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in IN, KA, Bengaluru.
The job was posted on September 9, 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

Fine-tuningNLPMachine learningDeep learningLLMsInformation RetrievalPythonAlexa

Also common in applied scientist jobs at this level

Generative AI Reinforcement learning TensorFlow Computer vision scikit-learn PyTorch

Pay

Typical applied scientist job: $167k

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

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?
Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where deep learning was part of your work. What did you do?
Tell me about a project where llms was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: Fine-tuning, NLP, Machine learning, Deep learning, and LLMs. 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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