Careers

The FAIM (Full Funnel Agentic Intelligence and Models) builds the agentic AI systems behind next-generation advertising. The team develops multimodal foundation models and LLM-driven agents that power shopper understanding, audience-aware creative generation, and agentic advertising workflows. As a Principal Applied Scientist for the FAIM team, you will invent the models that help advertisers achieve desired outcomes. Today's state of the art models can setup ad campaigns to a specification, but do not understand advertising strategy well enough to help with abstract goals that real advertisers ask about like "how can I make more money." This is a rare charter to build foundational optimization science where little exists today, spanning campaign recommendation, cross-product budget allocation, incrementality measurement, and long-term-sales modeling. You will set technical direction for a growing team, partner with engineering and product to take models from research to production at Amazon scale, and directly move advertiser ROAS and new-to-brand growth. In this role you will - Own the science vision for developing evaluations of the Ads Agent - Partner with scientists from across Ads to develop best practices for evaluating agent performance both online and offline - Raise the science bar for all agent evaluations developed in Ads. - Own establishing a data driven foundation for making decisions about when new features are ready to launch in Ads Agent. Key job responsibilities - Define the long-term scientific vision for agent evaluations in Ads - Translate ambiguous advertiser needs and competing objectives into a concrete science roadmap. - Invent, prototype, and productionize machine learning and optimization solutions for joint budget allocation across sponsored ad products, spanning the shopper journey from awareness to purchase. - Develop rigorous approaches to incrementality measurement and long-term-sales modeling that ground optimization in true advertiser value. - Design and lead large-scale experiments and analyses to validate hypotheses and guide product direction. - Partner closely with engineering and product to define technical contracts, data schemas, and serving systems that carry models into production. - Raise the technical bar across science and engineering through mentorship, design reviews, and hands-on collaboration. - Grow scientific talent and publish impactful research internally and at top-tier venues.

We're building systems that turn research into real world impact. We're advancing the intelligence behind how customers converse, connect and interact with Alexa across the globe and we're tackling complex conversational AI challenges with state of the art GenAI systems. We're looking for PhD students across multiple research domains to invent, design, and implement state of the art solutions for never before solved problems. Your work here won't just stay in a notebook, it ships to production and reaches customers worldwide. Check out the details below including the job responsibilities, team details, and basic qualifications before submitting your application. You can find more information about the Amazon Science community as well as interview preparation tips via the links below; - https://www.amazon.science/ - https://amazon.jobs/content/en/career-programs/university/science - https://amazon.jobs/content/en/how-we-hire/university-roles/applied-science Key job responsibilities As an Applied Science Intern, you will own the design and development of end-to-end systems. You'll have the opportunity to write technical white papers, create roadmaps and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists and other science interns to develop solutions and deploy them into production. You will have the opportunity to design new algorithms, models, or other technical solutions whilst experiencing Amazon's customer focused culture. The ideal intern should have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. A day in the life You'll spend your first weeks scoping your project with your mentor, then own the research and implementation end-to-end. Projects may include developing and implementing real-time AI services, conducting comprehensive analysis of GenAI algorithms and methodologies or contributing to the creation of reliable, efficient code for production systems that serve millions of users — depending on the team you're matched with. Many interns publish at top-tier conferences or see their work deployed to production before the internship ends. Interns may also be considered for a return offer at the end of their internship, subject to performance evaluation and headcount availability. Further benefits of an Amazon Science internship include; - All of our internships offer a competitive salary - Interns are paired with an experienced manager and mentor(s) - Interns get invited to different intern program or office events - Interns can build their professional and personal network with other Amazon Scientists - Interns can potentially publish work at top tier conferences About the team You'll submit a single application and we'll match you with science teams best aligned with your research interests. Applications are reviewed on a rolling basis, and your application stays active until we find a team match or confirm there are no matches available. Start dates are available throughout the year for durations of between 3–6 months. Please note, each team has different start date and duration preferences — your recruiter will confirm the preferences of the team you're matched with prior to interviewing. We offer science internships in multiple locations across the EMEA region and you can indicate your interest in all these locations by applying here (Austria, Estonia, France, Germany, Ireland, Israel, Italy, Jordan, Luxembourg, Netherlands, Poland, Romania, South Africa, Spain, Sweden, UAE, and UK). Please note we do not offer remote internships.

Are you passionate about using AI to protect and elevate the shopping experience for hundreds of millions of customers? We're looking for a Sr. Applied Science Manager to lead the Sponsored Products and Brands, Search Relevance team, ensuring that every product recommendation shoppers see on our search results page is relevant, trustworthy, and genuinely useful. In this role, you'll be at the forefront of defining what a great search experience looks like by inventing entirely new ways to help customers understand why a product matches their needs. Safeguard the Customer Experience - Lead the development of predictive models that identify and mitigate irrelevant, misleading, or low-quality product recommendations across search placements - Design and scale AI-powered systems whose outputs are used to continually improve the search results - Expand the definition of search quality beyond query-ASIN relevance to encompass shopper preferences, page-level context, and overall experience coherence Define and Measure Search Quality at Scale - Own the development and ongoing measurement of customer experience metrics as reported to senior leadership - Partner with leaders across Search, Retail, and Advertising organizations to align on metric definitions and establish independent measurement standards where needed - Build scalable measurement frameworks that anticipate evolving search presentation formats and proactively adapt to them Invest in State-of-the-Art AI Infrastructure - Drive investment in AI infrastructure and systems that reduce the cost of measuring and monitoring CX metrics across all placements and marketplaces - Build increasingly precise AI models that minimize human effort for auditing, training data generation, and placement-specific customization - Make strategic build-vs-reuse decisions by staying closely connected to peer teams across the organization Invent New Product Experiences - Create novel product presentation experiences that help shoppers understand the differential value of each result — through intelligent selection of images and titles, contextual explanations, and visual indicators such as badges and tags - Partner with peer teams to share learnings and advocate for the adoption of successful innovations beyond your immediate scope Lead and Influence Across the Organization - Manage and grow a team of applied scientists, engineers, and product managers - Partner with senior leaders (Director-level and above) across multiple organizations to drive alignment on shared goals and cross-functional initiatives - Represent search quality and customer experience in executive-level reviews and reporting cadences

Do you want to create the greatest-possible worldwide impact in Robotics? Amazon has the world's most exciting treasure trove of robotics challenges. At Amazon Robotics we build high-performance, real-time robotic systems that can perceive, learn, and act intelligently alongside humans—at Amazon scale. Amazon Robotics invents and scales AI systems for robotics in fulfillment. Our mission is to enable robots to interact safely, efficiently, and fluently high density real-world fulfillment centers. Our AI solutions enable robots to learn from their own experiences, from each other, and from humans to build intelligence that feeds itself. We hire and develop subject matter experts in AI with a focus on 3D perception, computer vision, deep learning, and generative modeling. We target high-impact algorithmic unlocks in areas such as 3D scene understanding and completion, semantic occupancy prediction, multi-view 3D reconstruction, depth estimation, shape completion, and real-time inference - all of which have high-value impact for our current and future fulfillment networks. We are seeking an passionate, hands-on, seasoned Senior Applied Scientist who will be deep in code and algorithms; who is technically strong in building scalable 3D perception systems across semantic scene completion, encoder-decoder and transformer architectures (e.g., VoxFormer, MonoScene), voxelized occupancy prediction, panoptic and instance segmentation, depth estimation, point cloud processing, and multi-view fusion. As a Senior Applied Scientist, you will contribute to the research and development of advanced 3D perception pipelines that enable robots to reason about occluded and partially observed environments; your work along with other top-notch scientists and engineers will deliver the world's most scalable and robust robotic perception systems. You will drive ideas to products using paradigms such as 3D generative models, query-based transformers, masked autoencoder-style completion, and scalable pseudo-ground-truth data generation. As a Senior Applied Scientist, you will also help lead and mentor our team of applied scientists and engineers. You will take on challenging perception problems - such as completing 3D bin scenes from partial observations, integrating multi-camera inputs, and optimizing inference latency for edge deployment - distill requirements, and then deliver solutions that either leverage existing academic and industrial research or utilize your own out-of-the-box but pragmatic thinking. In addition to coming up with novel solutions and prototypes, you will directly contribute to implementation while you lead. A successful candidate has excellent technical depth in 3D computer vision, scientific vision, project management skills, great communication skills, and a drive to achieve results in a collaborative team environment. You should enjoy the process of solving real-world problems that, quite frankly, haven't been solved at scale anywhere before. Along the way, we guarantee you'll get opportunities to be a disruptor, prolific innovator, and a reputed problem solver—someone who truly enables AI and robotics to significantly impact the lives of millions of consumers. Key job responsibilities Architect, design, and implement 3D perception models - including encoder-decoder networks, query-based transformers, and generative architectures- for semantic occupancy prediction and scene completion on robotic platforms. Own the end-to-end model lifecycle: develop scalable training pipelines, optimize inference latency for ARM-based edge processors, and deploy production models that meet real-time performance targets. Design and scale pseudo-ground-truth data generation pipelines - both heuristic-based and learning-based (e.g., SAM3D, shape completion) to produce curated training samples using SageMaker infrastructure. Drive multi-view perception integration by fusing multiple view camera inputs for robust 3D reconstruction in partially observed and occluded bin environments. Influence the team's technical strategy and contribute to the long-term vision and roadmap for 3D perception in fulfillment robotics. Partner with cross-functional stakeholders across engineering, science, and operations teams to define requirements, iterate on system design, and deliver end-to-end solutions from research prototype to production deployment. Maintain high standards by participating in design and code reviews, designing for fault tolerance and operational excellence, and creating mechanisms for continuous improvement. Prototype and validate concepts through simulation, synthetic data evaluation, and live robotic workcell testing using 3D metrics (mIoU, IoU) and affordance-based evaluation frameworks. Mentor applied scientists and engineers, raise the technical bar, and foster a culture of scientific rigor and rapid experimentation. A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners. 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!

At Audible, we believe stories have the power to transform lives. It’s why we work with some of the world’s leading creators to produce and share audio storytelling with our millions of global listeners. We are dreamers and inventors who come from a wide range of backgrounds and experiences to empower and inspire each other. Imagine your future with us. ABOUT THIS ROLE As a Senior Applied Scientist, you will solve large complex real-world problems at scale, draw inspiration from the latest science and technology to empower undefined/untapped business use cases, delve into customer requirements, collaborate with tech and product teams on design, and create production-ready models that span various domains, including Machine Learning (ML), Artificial Intelligence (AI) and Generative AI, Natural Language Processing (NLP), Reinforcement Learning (RL), real-time and distributed systems. ABOUT YOU Your work will focus on inventing and extending scientific approaches, models, and algorithms driven by customer needs at the product level, framing new research problems even when the problem is ill-defined and no textbook solution exists. You will lead the design, implementation, and delivery of scientifically complex, end-to-end solutions that are deployed into production, defining system-level requirements and writing a significant portion of the critical-path code. You will develop reusable science components and services that resolve architecture deficiencies and customers’ pain points, while making technical trade-offs for long-term/short-term. You will work independently with limited guidance, and your decision-making will consistently incorporate robust, data-driven business and technical judgment. You will drive your team’s scientific agenda, author internal or external peer-reviewed publications that validate the novelty of your work, mentor and develop other scientists, and build consensus across multiple teams. You will have the opportunity to innovate, invent, and think big, and influence the experiences of millions of customers. We are looking for a results-oriented Senior Applied Scientist with deep expertise in ML, NLP, Deep Learning, GenAI, and/or large-scale distributed computation. As an Applied Scientist, you will... - Understand complex, ambiguous use cases across the business and adopt/extend/design/invent solutions/models that are scalable, efficient, and automated, where neither the problem nor the solution is well defined - Work closely with fellow scientists and software engineers (at Audible and Amazon) to build and productionize models, and deliver novel and highly impactful features - Review models of peers for the purpose of reducing and managing risk to the business, while improving customer experience - Lead the design, development, and production deployment of scientifically complex, end-to-end solutions for Content Understanding, Recommendations, and GenAI-based product features, defining system-level requirements - Drive and lead initiatives that employ the most recent advances in ML/AI/GenAI, drive your team’s scientific agenda, and author peer-reviewed publications - Mentor and grow scientists on the team and across Amazon, and push the boundary of innovation ABOUT AUDIBLE Audible is the leading producer and provider of audio storytelling. We spark listeners’ imaginations, offering immersive, cinematic experiences full of inspiration and insight to enrich our customers daily lives. We are a global company with an entrepreneurial spirit. We are dreamers and inventors who are passionate about the positive impact Audible can make for our customers and our neighbors. This spirit courses throughout Audible, supporting a culture of creativity and inclusion built on our People Principles and our mission to build more equitable communities in the cities we call home.

Come shape the future of robotic sensing hardware by building the computer vision and ML systems that define how every robot in Amazon's global fulfillment network sees, senses, and understands the world. This is applied science embedded in a hardware development team where you directly shape what gets built, manufactured, and deployed at scale. As a Senior Applied Scientist on the Amazon Robotics Sensing and Compute hardware team, you will be the ML and perception expert working alongside the Principal Applied Scientist and Principal Hardware Development Engineer to shape the future of deployed and scaled sensing hardware. You will influence sensor selection, compute architecture, optical design, and system level trade offs, ensuring the hardware we build is purpose designed for the algorithms that run on it. You will help define what the next generation of cameras, depth sensors, and edge compute modules need to be, and then prove it with data, experimentation, and production validation. What makes this role unique: -Embedded in the hardware development team, directly influencing what gets designed, manufactured, and shipped -Partner with the Principal Engineers to co define the sensing and compute hardware roadmap for every major Amazon Robotics program -Own the ML experimentation and validation that drives hardware architecture decisions for cameras, depth sensors, and edge compute -Solve perception problems no other company is tackling: safety rated vision, multi modal sensor fusion, and real time inference on resource constrained hardware -Ship to hundreds of fulfillment sites worldwide across autonomous mobile robots, manipulation workcells, barcode sensing, and safety rated human collaborative systems -Your work does not sit in a research queue. It goes into production hardware that enables the future of Amazon Robotics Key job responsibilities -Partner with the Principal Hardware Development Engineer and Principal Applied Scientist to co define sensing and compute hardware requirements, ensuring ML and perception needs drive sensor selection, optical design, compute architecture, and system level trade offs -Develop and optimize computer vision and deep learning models for robotic perception including object detection, depth estimation, scene understanding, and barcode recognition, purpose built for edge deployment on resource constrained hardware -Lead ML experimentation and validation that directly informs hardware architecture decisions for cameras, depth sensors, and edge compute modules -Design and execute benchmarking frameworks to evaluate sensing hardware performance across diverse robotic use cases, providing data driven recommendations on what to build next -Optimize models for production edge deployment including quantization, pruning, distillation, and inference pipeline tuning to meet strict latency, power, and accuracy requirements -Drive research and experimentation on next generation perception capabilities in collaboration with the Principal Applied Scientist, including multi modal sensor fusion, safety rated vision, and real time inference for autonomous and human collaborative robotics -Collaborate cross functionally with hardware engineers, software developers, and robotics program teams to transition models from prototype through production validation and fleet deployment -Contribute to the sensing and compute hardware roadmap alongside the Principal Applied Scientist, identifying where ML trends and emerging capabilities require new hardware architecture or sensor modalities -Mentor engineers and scientists on ML best practices for embedded systems and production robotics -Publish and present research to advance the state of the art in robotic perception A day in the life In this role you will work at the intersection of machine learning and physical robotics hardware. You will collaborate with hardware engineers to evaluate how your models perform on real sensors and edge compute, run experiments that directly influence what gets built next, and validate perception systems alongside robotics program teams. You will push the boundaries of computer vision for robotic applications while seeing your work deploy on real robots in real fulfillment centers. This is applied science with immediate, measurable impact at a scale no other robotics company can offer. Amazon offers a full range of benefits that support you and eligible family members, including domestic partners. 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 The Amazon Robotics Sensing and Compute team powers a customer centric ecosystem built for speed, quality, and innovation. We stay ahead of sensing and compute technology, standardize reusable and cost effective modules, and pair them with deep engineering expertise and a robust supply chain. Every customer gets the right solution, whether off the shelf, rapidly customized, or purpose built, easy to integrate and ready to scale. We are a small, fast moving team of builders who own the full stack from optics through edge compute and take pride in shipping production hardware that enables every major robotics program in Amazon's global fulfillment network. If you want your work to matter on day one, this is the team.

Amazon Global Fleet and Products (GFP) organization is responsible for fleet programs and capacity. The Fleet Planning team is looking for a Data Scientist to drive the most efficient use of fleet. Amazon Middle and Last Mile fleet planning is a complex resource allocation problem. The goal of fleet allocation planning is to optimize the size and mix of fleet allocated to various programs through. Changes in routes, network, exiting DSPs and new DSP onboarding create continuous need for re-allocation of fleet to maintain an efficient network capacity. This requires allocation to adhere to various operational limits (repair network, EV range, Station Charging capability) and also match route’s cube need to vehicles capacity. As a Data Scientist on the Fleet Planning team (GFP), you will be responsible for building new science models (linear programs, statistical and ML models) and enhancing existing models for changing business needs. You would work with program managers in planning, procurement, redeployment, deployment, remarketing, variable fleet and infrastructure programs to build models that would support the requirements of all programs in a coherent plan. Key job responsibilities • Build models and automation for planners for generating vehicle allocation plans • Partner with program teams to test and measure success of implemented model • Lead reviews with senior leadership, deep dive model outputs and explain implications of model recommendations.

Are you passionate about solving complex logistics challenges? Our Analytics team is at the forefront of enhancing delivery experiences through data-driven solutions and innovative technology. As a Research Scientist, you will join a team dedicated to optimizing our delivery network, ensuring reliable and efficient service to our customers. We are seeking an enthusiastic, customer-centric professional with strong analytical capabilities to drive impactful projects, implement advanced solutions, and develop scalable processes. In this role, you will have immediate ownership of business-critical challenges and the opportunity to make strategic, data-driven decisions that shape the future of our delivery operations. Your work will directly influence customer experience and operational excellence. The ideal candidate will possess both research science capabilities and program management skills, thriving in an environment that requires independent decision-making and comfort with ambiguity. This role offers the opportunity to make a significant impact on our advanced logistics network while working with pioneering technology and data science applications. Basic qualifications • 3+ years of building machine learning models for business application experience • Knowledge of programming languages such as C/C++, Python, Java or Perl • Experience programming in Java, C++, Python or related language • Experience with neural deep learning methods and machine learning Preferred qualifications: • PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field • 5+ years of extensive relevant research experience • Deep expertise in Machine Learning • Proficiency in programming • Core competency in mathematics and statistics • Track record of successful projects in algorithm design and product development • Publications at peer-reviewed conferences or journals • Strategic thinker with good execution skills • Exhibits excellent business judgment • Effective verbal and written communication skills • Experience working with real-world data sets and building scalable models from big data • Experience with modern modeling tools and frameworks such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow • Experience with large scale distributed systems

We are seeking an Applied Scientist to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this role, you will focus on designing how we measure, stress-test, and improve robot behavior across a wide range of real-world tasks. Your work will play a critical role in shaping how policies are validated and how high-quality datasets are generated to accelerate system performance. You will operate at the intersection of robotics, machine learning, and human-in-the-loop systems, building the infrastructure and methodologies that connect teleoperation, evaluation, and learning. This includes developing evaluation policies, defining task structures, and contributing to operator-facing interfaces that enable scalable and reliable data collection. The ideal candidate is highly experimental, systems-oriented, and comfortable working across software, robotics, and data pipelines, with a strong focus on turning ambiguous capability goals into measurable and actionable evaluation systems. Key job responsibilities - Design and implement evaluation frameworks to measure robot capabilities across structured tasks, edge cases, and real-world scenarios - Develop task definitions, success criteria, and benchmarking methodologies that enable consistent and reproducible evaluation of policies - Create and refine data collection protocols that generate high-quality, task-relevant datasets aligned with model development needs - Build and iterate on teleoperation workflows and operator interfaces to support efficient, reliable, and scalable data collection - Analyze evaluation results and collected data to identify performance gaps, failure modes, and opportunities for targeted data collection - Collaborate with engineering teams to integrate evaluation tooling, logging systems, and data pipelines into the broader robotics stack - Stay current with advances in robotics, evaluation methodologies, and human-in-the-loop learning to continuously improve internal approaches - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Our work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but experience. We’re building robots that feel responsive, expressive, and genuinely useful. At Fauna, you’ll work at the frontier of this space, helping define how robots move, manipulate, and interact with people in natural environments. It’s an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you.

Do you want to lead the Ads industry and redefine how we measure the effectiveness of Amazon Ads business? Are you passionate about causal inference, Deep Learning & AI, raising the science bar, and connecting leading-edge science research to Amazon-scale implementation? If so, come join Amazon Ads to be a science leader within our Advertising Incrementality Measurement science team! Our work builds the foundations for providing customer-facing advertising measurement tools, furthering internal research & development, and building out Amazon's advertising measurement offerings. Incrementality is a lynchpin for the next generation of Amazon Advertising measurement solutions, and this role will play a key role in the release and expansion of these offerings. We are looking for a thought leader that has an aptitude for delivering customer-focused solutions and who enjoys working on the intersection of Big-Data analytics, Machine/Deep Learning, and Causal Inference. A successful candidate will be a self-starter, comfortable with ambiguity, able to think big and be creative, while still paying careful attention to detail. You should be able to translate how data represents the customer journey, be comfortable dealing with large and complex data sets, and have experience using machine learning and/or econometric modeling to solve business problems. You should have strong analytical and communication skills, be able to work with product managers to define key business questions and work with the engineering team to bring our solutions into production. You will join a highly collaborative and diverse working environment that will empower you to shape the future of Amazon advertising, and also allow you to become part of our large science community. Key job responsibilities • Apply expertise in ML/DL, AI, and causal modeling to develop new models that describe how advertising impacts customers’ actions • Own the end-to-end development of novel scientific models that address the most pressing needs of our business stakeholders and help guide their future actions • Improve upon and simplify our existing solutions and frameworks • Review and audit modeling processes and results for other scientists, both junior and senior • Work with leadership to align our scientific developments with the business strategy • Identify new opportunities that are suggested by the data insights • Bring a department-wide perspective into decision making • Develop and document scientific research to be shared with the greater science community at Amazon About the team AIM is a cross disciplinary team of engineers, product managers, economists, data scientists, and applied scientists with a charter to build scientifically-rigorous causal inference methodologies at scale. Our job is to help customers cut through the noise of the modern advertising landscape and understand what actions, behaviors, and strategies actually have a real, measurable impact on key outcomes. The data we produce becomes the effective ground truth for advertisers and partners making decisions affecting millions in advertising spend.