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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.

Amazon's Customer Experience and Business Trends (CXBT) organization is hiring a Senior Data Scientist for its Benchmarking, Economics, Analytics and Measurement (BEAM) team. BEAM's mission is to improve customer experience across every Amazon business by turning fragmented internal and external data into decision-grade intelligence for senior leaders. This role anchors our Topline View of Retail initiative: an always-on competitive intelligence capability that fuses third-party transaction panels with Amazon's internal signals to answer how Amazon is winning or losing share of wallet, across segments, categories, and time. You will not just analyze metrics — you will decide which metrics should exist, then build the statistical and machine learning models that generate, forecast, and explain them. Given a new business question and a pile of raw panel and internal data, you are the person who defines what "competitiveness" means, translates it into a defensible, reproducible metric and model, and defends the methodology to skeptical senior stakeholders. We are looking for someone with exceptional business judgment and metric intuition, paired with deep hands-on modeling skills: a strong point of view on what to measure, and the technical ability to build the models that measure it at scale. You should be equally comfortable pressure-testing a model's assumptions and explaining its "so what" to a VP in two sentences. Key job responsibilities - Own the definition, methodology, and evolution of BEAM's competitiveness metrics (e.g., share-of-wallet, segment-level penetration, price/selection/fulfillment competitiveness), including the trade-offs behind each definition. - Design, build, and validate statistical and machine learning models — forecasting, causal inference, segmentation, and anomaly detection — that power those metrics and surface competitive shifts. - Pull together disparate third-party transaction-panel data and internal Amazon signals into coherent, reproducible modeling pipelines that leadership can trust for recurring reporting. - Translate ambiguous business questions ("are we losing ground in grocery?") into the right metric, the right model, the right cut of data, and a clear, defensible answer. - Productionize models and analyses so they run reliably as recurring mechanisms, partnering with data engineers to scale and automate. - Produce monthly flash reports and deep-dive narratives that turn models and metrics into decisions for CXBT and business-line leadership. - Set the standard for analytical and modeling rigor on the team — methodology reviews, documentation, and guardrails against misleading cuts. - Mentor and technically uplift other scientists and BIEs on the team — reviewing modeling approaches and raising the bar on rigor (e.g., partnering with our Data Scientist on the MatchIQ model to strengthen methodology and validation). About the team CXBT is a group of diverse functions dedicated to understanding, improving, and influencing customer experience globally, across all of Amazon. We are builders who develop products, services, and data-driven approaches that shape offerings for nearly every Amazon business and customer type — consumers, developers, sellers, brands, employees, investors, streamers, and gamers. We work backwards from customer needs, combine technical and non-technical methods, and track industry and business trends. Our roles span Product Managers, Data Scientists, Economists, Business Intelligence Engineers, Data Engine, Applied Scentist, and Product Manager.

Lead a team of scientists who measure what matters—the real-world impact of Amazon’s marketing at billion-dollar scale. Amazon Advertising is one of Amazon’s fastest growing lines of business and our team occupies a unique position focused on maximizing the success of Amazon’s own marketing programs using our ad tech. Amazon marketing is Customer Zero for our ad tech, and our measurement science team is focused on quantifying the impact of the marketing that drives customers to discover and engage with Amazon’s products and services. We turn petabytes of retail, streaming, and customer signals into causal insights that make Amazon’s marketing spend smarter and more effective. As Customer Zero, we also extend the impact of methodologies, measurement systems, and insights we generate for Amazon’s marketing to directly inform the products and services we offer to external advertisers, shaping the tools that power the broader Amazon Ads ecosystem. This is a growth role for a successful candidate that is energized by building high-performing teams, tackling causal inference challenges that don’t have textbook answers, and shipping science that improves both Amazon’s own marketing and the advertising products used by the world’s largest brands. Dozens of Amazon businesses use Amazon’s ad tech for their marketing objectives, driving more than $1B of marketing investments through Ads services and tools. As part of our team, this role will lead a cross-functional science team to measure the impact of Amazon’s marketing and identify opportunities for optimization at scale. You will set the strategic direction for your team, drive initiatives to make smarter marketing decisions, and improve the relevance of advertising to our customers. We move away from industry standard measurement systems and build sophisticated and insightful decision engines. We enable massive advertising programs, generating billions of impressions decorated with rich representations of customer state. The major challenges we are solving include integrating petabyte-scale distributed retail systems into a singular service to synthesize e-commerce data into measurement and optimization models. The successful candidate will have a strong technical background in ML and causal inference, experience with large scale data ML models, high judgment to drive business impact, an appreciation for white-space, and success building and developing high-performing teams. Key job responsibilities • Lead, hire, mentor, and develop a team of scientists and engineers, fostering a culture of innovation, scientific rigor, and operational excellence. • Set the technical vision and roadmap for the team’s measurement and modeling initiatives, aligning with business priorities. • Partner with senior leadership to define measurement strategies and translate business needs into science roadmaps and deliverables. • Guide the team in applying ML, statistics, and econometrics to develop, analyze, and productionize prototype models. • Drive the design and analysis of large-scale online experiments to validate models and measure impact. • Foster collaboration across science teams through peer-review processes, publishing research internally and at industry conferences. • Establish scalable, efficient, and automated processes for large-scale model development, validation, and implementation.

AWS Infrastructure Services Science (AISS) researches and builds machine learning models that influence the power utilization at our data centers to ensure the health of our thermal and electrical infrastructure at high infrastructure utilization. As an Applied Scientist, you will work on our Science team and partner closely with other scientists, data engineers and Software teams to accurately model and optimize our power infrastructure. Outputs from your models will directly influence our data center topology and will drive exceptional cost savings. You will be responsible for researching and deploying machine learning models that optimize our power and thermal infrastructure, working across AWS to solve data mapping and quality issues and contribute to our Science team vision. You are skeptical. When someone gives you a data source, you pepper them with questions about sampling biases, accuracy, and coverage. When you’re told a model can make assumptions, you proactively try to break those assumptions. You have passion for excellence. The wrong choice of data could cost the business dearly. You maintain rigorous standards and take ownership of the outcome of your data pipelines and code. You raise the bar on the software code standards to delivery faster and efficient solutions. You do whatever it takes to add value. You don’t care whether you’re building complex ML models, writing blazing fast code, integrating multiple disparate data-sets, or creating baseline models - you care passionately about stakeholders and know that as a curator of data insight you can unlock massive cost savings and preserve customer availability. You have a limitless curiosity. You constantly ask questions about the technologies and approaches we are taking and are constantly learning about industry best practices you can bring to our team. You have excellent business and communication skills to be able to work with product owners to understand key business questions and earn the trust of senior leaders. You will need to learn Data Center architecture and components of electrical engineering to build your models. You are comfortable juggling competing priorities and handling ambiguity. You thrive in an agile and fast-paced environment on highly visible projects and initiatives. The tradeoffs of cost savings and customer availability are constantly up for debate among senior leadership - you will help drive this conversation. This position requires superior analytical thinkers, able to quickly approach large ambiguous problems and apply their technical and statistical knowledge to identify opportunities for further research. You should be able to independently mine and analyze data, and be able to use any necessary programming and statistical analysis software to do so. Successful candidates must thrive in fast-paced environments which encourage collaborative and creative problem solving, be able to measure and estimate risks, constructively critique peer research, and align research focuses with the Amazon’s strategic needs. Key job responsibilities - Leverage deep expertise in one or more scientific disciplines to invent solutions to ambiguous ads measurement problems - Disambiguate problems to propose clear evaluation frameworks and success criteria - Work autonomously and write high quality technical documents - Implement a significant portion of critical-path code, and partner with engineers to directly carry solutions into production - Work closely with other scientists to deliver impactful customer solutions - Share and publish works with the broader scientific community through meetings and conferences - Communicate clearly to scientific audiences

As a Principal Applied Scientist at Prime Video, you will be a technical and strategic leader responsible for inventing, developing, and deploying groundbreaking AI solutions that power personalized, relevant, and delightful experiences for millions of global customers. You will help shape the vision and direction of key ML systems that support Prime Video’s mission to deliver AI-powered customer experiences. This role demands a unique blend of deep technical expertise in machine learning and recommendation systems, industry leadership, and strong collaboration skills. You will guide the development of high-impact systems end-to-end - leading innovation from foundational research through production deployment - while mentoring scientists and influencing product and engineering roadmaps. We are looking for a thought leader who brings a strong track record of delivering ML innovations at scale, along with the curiosity and drive to push boundaries. This is a rare opportunity to drive meaningful impact at one of the largest streaming services in the world. Key job responsibilities - Invent, prototype, and productionize large-scale AI solutions across Prime Video’s personalization and discovery ecosystem using deep learning, generative AI, reinforcement learning, and optimization techniques; - Provide technical leadership and influence product vision by collaborating closely with engineers, product managers, and senior stakeholders; - Design and lead high-impact A/B tests and data analyses to validate hypotheses and guide product direction; - Drive technical bar-raising across science and engineering teams through mentorship, design reviews, and collaboration; - Stay ahead of industry trends and emerging research; leverage them to evolve long-term strategy and architecture; - Publish impactful research internally and externally (e.g. top-tier conferences and journals).

Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video subscriptions such as Apple TV+, HBO Max, Peacock, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video team member, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Prime Video is pioneering the use of Generative AI to empower the next generation of creatives. Our mission is to make world-class media creation accessible, scalable and efficient. We are seeking an Applied Scientist to advance the state of the art in Generative AI and to deliver these innovations as production-ready systems at Amazon scale. Your work will give creators unprecedented freedom and control while driving new efficiencies. Key job responsibilities As an Applied Scientist, you will have end-to-end ownership of the product, related research and experimentation. In addition, you will be applying advanced machine learning techniques in Computer Vision, Multimedia Understanding and Generative AI. We're building the foundational technology stack, spanning diffusion and flow-matching models, 3D/4D scene and character generation, motion and camera control, and post-training alignment. Other responsibilities include: - Research and develop generative models for controllable synthesis across images, video, vector graphics, and multimedia - Innovate in advanced diffusion and flow-based methods (e.g., inverse flow matching, parameter efficient training, guided sampling, test-time adaptation) to improve efficiency, controllability, and scalability - Advance visual grounding, depth and 3D estimation, segmentation, and matting for integration into pre-visualization, compositing, VFX, and post-production pipelines - Design multimodal GenAI workflows including visual-language model tooling, structured prompt orchestration, agentic pipelines

We are seeking an Applied Scientist to join Amazon Robotics, Compass Team. In this role, you will own the development of safe legged locomotion algorithms and their deployment on physical hardware, developing learning-based controllers that enable quadrupeds and humanoids to walk, run, and recover from disturbances with agility and robustness. You will leverage Reinforcement Learning (RL), sim-to-real transfer, and other learning-based architectures to train policies that produce stable, dynamic gaits across varied terrains and operating conditions. These learned policies will interface with model-based control strategies to form whole-body control laws that balance performance and safety. Your work sits at the novel intersection of safety and machine learning, where these learned policies will be used in a safety-critical context for complex safety constraints like stability. You will collaborate closely with perception, planning, and safety teams to close the loop between what the robot sees, where it needs to go, and how it moves to get there safely. This is a rare opportunity to shape how legged robots move through the world alongside people. Key job responsibilities • Design, train, and deploy reinforcement learning policies for dynamic legged locomotion including walking, running, stair climbing, and fall recovery on physical quadruped and humanoid platforms • Collaborate with the Compass safety team to ensure locomotion policies operate within safety-critical bounds, incorporating control barrier functions or other formal safety mechanisms as constraints during or after training • Develop sim-to-real transfer pipelines that produce policies robust to the reality gap, including domain randomization, system identification, and adaptive strategies • Integrate learned locomotion policies with model-based whole-body controllers, defining how RL outputs (e.g., joint targets, contact schedules) interface with optimization-based control layers • Formulate reward functions and training curricula that encode both performance objectives and safety constraints, ensuring policies respect stability and contact-force limits • Develop and maintain large-scale training infrastructure for locomotion policy learning, including physics simulation environments and parallelized training pipelines • Evaluate policy performance rigorously through simulation benchmarks, hardware experiments, and failure-mode analysis • Investigate emerging techniques (e.g., foundation models for control, diffusion policies, world models) and assess their applicability to safe legged locomotion • Publish research at top-tier robotics and ML venues and contribute to Amazon's scientific reputation in legged robotics • Collaborate with perception and planning teams to enable terrain-aware and goal-conditioned locomotion behaviors A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team Work with the inventors of control barrier functions on a novel, universal approach to safe autonomy: one that scales across mobile robots, manipulators, mobile manipulators, and future robot platforms with dynamic stability. You'll push the boundary of safe performance by integrating safety with motion planning, RL, and foundation models, ensuring that safety is never a blocker to robot performance. Your work will underpin robots operating alongside people at Amazon's unprecendented scale.

Lead ML teams building large-scale forecasting and optimization systems that power Amazon’s global transportation network and directly impact customer experience and cost. As an Sr Applied Scientist, you will set scientific direction, mentor applied scientists, and partner with engineering and product leaders to deliver production-grade ML solutions at massive scale. Key job responsibilities 1. Lead and grow a high-performing team of Applied Scientists, providing technical guidance, mentorship, and career development. 2. Define and own the scientific vision and roadmap for ML solutions powering large-scale transportation planning and execution. 3. Guide model and system design across a range of techniques, including tree-based models, deep learning (LSTMs, transformers), LLMs, and reinforcement learning. 4. Ensure models are production-ready, scalable, and robust through close partnership with stakeholders. Partner with Product, Operations, and Engineering leaders to enable proactive decision-making and corrective actions. 5. Own end-to-end business metrics, directly influencing customer experience, cost optimization, and network reliability. 6. Help contribute to the broader ML community through publications, conference submissions, and internal knowledge sharing. A day in the life Your day includes reviewing model performance and business metrics, guiding technical design and experimentation, mentoring scientists, and driving roadmap execution. You’ll balance near-term delivery with long-term innovation while ensuring solutions are robust, interpretable, and scalable. Ultimately, your work helps improve delivery reliability, reduce costs, and enhance the customer experience at massive scale.

As a member of a growing PNT team within Amazon Leo, the PNT Estimation and Algorithms Engineer is responsible for designing, implementing, optimizing and monitoring high-precision estimation architectures across terrestrial and space-based networks. You will develop and validate orbit determination, filtering, and sensor fusion algorithms to achieve high precision positioning and timing accuracy using RF and optical inter-satellite link measurements. Export Control Requirement Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. Key job responsibilities Design, implement, and validate orbit determination (OD) and clock estimation algorithms, including Extended Kalman Filters (EKF), Batch Least Squares, Square-Root/Sigma-Point Kalman Filters (SKF), and Factor Graph Optimization (FGO) approaches Develop inter-satellite link (ISL) navigation and time-transfer measurement models, including range and range-rate Evaluate and integrate emerging estimation techniques (batch/sequential estimation, robust filtering, multi-sensor fusion) to improve positioning and timing accuracy Design and build simulation and analysis frameworks (e.g., Monte Carlo CONOPS studies) to characterize filter performance, covariance realism, and convergence behavior Collaborate with cross-functional teams to ensure estimator outputs meet mission-level accuracy requirements Conduct performance analysis and troubleshooting of estimation algorithms across distributed constellation and ground infrastructure

Within Amazon's North American Operations, we are tackling mission-critical problems to optimize network operations. We build complex causal, forecasting, and optimization models to better understand, predict, and govern processes across hundreds of warehouses. We are seeking a Data Scientist to drive the development of these models, deeply integrate with business stakeholders, and leverage generative AI to more intelligently build for our customers. Key job responsibilities - Interfacing with stakeholders to understand business problems and translating these into high and low-level design documents. - Building machine learning, optimization, and causal inference models that target business-specific challenges. - Sourcing, cleaning, and analyzing large and complex data sets associated with warehouse operations to provide business-critical insights at scale. - Leveraging AI-based solutions to drive development and provide nuanced insights into model outcomes. A day in the life You will be a single-threaded owner of projects within Amazon Operations, owning project scoping, stakeholder engagement, data enablement, and model development. Your work will be directly consumed by partner teams throughout Operations and Finance, and hence a strong sense of ownership is required to educate both technical and non-technical stakeholders on developed science solutions. About the team Our team is comprised of Data Scientists, Applied Scientists, and Economists, and are trusted to make data-driven solutions that power operations decision making. We strive to build consensus on divisive topics through clear presentation of problem statements paired with deep analyses that gain visibility throughout all levels of the organization from operators to executives.