Senior Applied Scientist, Search Science

- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.

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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 looking for an execution-focused Senior Applied Scientist to spearhead core scientific initiatives within our Bangalore hub. In this leadership-by-example role, you will define and execute the applied science roadmap for search and content discovery systems, directly impacting millions of customers worldwide.

You will operate at the exciting intersection of large-scale search infrastructure, applied machine learning, and foundation models. You will drive immediate, high-impact business deliverables in music search relevance, personalization, and retrieval performance, while simultaneously architecting the long-term technological vision that expands our search ecosystem toward deeper semantic intelligence, agentic workflows, and cross-domain audio understanding.

Key job responsibilities
- Strategic Roadmap & Architecture: Define and execute the technical and scientific roadmap for music search systems, making critical architectural decisions that balance short-term feature delivery with long-term scalability, maintainability, and extensibility.

- Pioneering Applied Science: Lead the design and implementation of state-of-the-art machine learning solutions leveraging deep learning, LLMs, and agentic workflows to solve complex search, ranking, and intent-matching challenges.

- Cross-Functional Leadership: Partner closely with product management, engineering leaders, and peer teams to harmonize technical direction and deliver synchronized customer experiences.

- Technical Excellence & Mentorship: Drive engineering and scientific excellence across the team by conducting rigorous design reviews, establishing modeling best practices, setting high bars for artifact delivery, and mentoring junior/mid-level scientists.

- Experimentation & Scaling: Establish robust scientific processes for large-scale data analysis, offline model validation, and online A/B experimentation, ensuring high statistical rigor and measurable business impact across millions of active listeners.

- Stakeholder Influence & Writing: Author strategic whitepapers, and executive-level documentation. Communicate complex technical options and trade-offs to senior leadership to drive informed decision-making.

Basic Qualifications
- PhD, or Master’s degree and 6+ years of applied research and industrial machine learning experience in Computer Science, Machine Learning, or a related field.
- 3+ years of specialized experience designing, building, and scaling machine learning models for core production business applications (e.g., Search, Recommendation Systems, or Large-Scale Information Retrieval).
- Expert programming proficiency in Python, Java, C++, or related languages, combined with deep familiarity with neural network architectures and deep learning frameworks.
- Proven track record of owning end-to-end technical deliverables from problem formulation and model architecture to production deployment and performance tuning.
- Demonstrated leadership in mentoring technical talent and driving engineering/scientific best practices.

Preferred Qualifications
- Deep expertise in search retrieval, query understanding, ranking algorithms, and large-scale vector search/embedding systems.
- Experience building applied science solutions on top of foundation models, large language models (LLMs), or multi-modal architectures.
- Experience with large-scale distributed training and inference optimization on cloud infrastructure (AWS).
- A strong publication record or patent portfolio in top-tier peer-reviewed venues (e.g., NeurIPS, SIGIR, KDD, ACL, ICML).
- Experience designing and interpreting complex online experimentation frameworks for consumer-facing recommendation or search products.