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Text of the page (random words):
nesses run their contact centers more effectively we work closely with product engineering and science partners to turn research ideas into features that customers rely on every day we value curiosity collaboration and scientific rigor and we are investing in new ai capabilities that will continue to transform the customer service industry if you want to see your research make a tangible impact at scale this is the place to do it applied scientist scot fo snt es b barcelona how does amazon decide which fulfillment center ships your order which truck carries it and how to keep promises across hundreds of millions of packages daily how does it decide how many trucks and how much labor are required to ship orders across the network scot fulfillment optimization fo owns the optimization and forecasting science behind these decisions we are seeking applied scientists to join the fo science tech team in barcelona alternatively luxembourg or london with a strong academic background in optimization machine learning and or time series forecasting you will design and build state of the art machine learning and optimization models that power amazon s fulfillment decisions at an unprecedented scale across two core scientific pillars large scale optimization and planning designing planning systems for order assignment and resource utilization while balancing multi objective cost speed tradeoffs to enable controllers to steer millions of shipments per hour optimally demand forecasting predictive ml developing time series forecasts for customer demand incorporating contextual information weather sales order properties and modeling uncertainty for core planning systems basic qualifications phd in operations research applied mathematics computer science or related field or equivalent experience strong programming skills python preferred experience with optimization solvers a plus research experience in one or more large scale mathematical programming lp mip decomposition methods combinatorial optimization assignment scheduling network flows multi objective optimization and control large scale time series forecasting genai models probabilistic forecasting uncertainty quantification causal inference spatiotemporal causal modeling offline policy evaluation preferred qualifications experience building optimization systems that run in production at scale being comfortable with ambiguity and fast iteration cycles publications in relevant venues key job responsibilities design and implement optimization and forecasting models for large scale fulfillment problems from order assignment to network flow control build research prototypes end to end from problem formulation through scalable implementation to production validation analyse complex tradeoffs cost speed capacity accuracy and translate findings into actionable recommendations for leadership and operations teams collaborate with engineers to bring science solutions into production systems serving millions of customer orders daily a day in the life you formulate an optimization or forecasting problem on a whiteboard with teammates then prototype it in python with real data by the afternoon you run experiments against production scale datasets iterate on the model and present results to stakeholders who will use them to make network decisions next week some days you dive deep into solver performance other days you re explaining a pareto frontier to an operations leader you collaborate with large engineering and product teams to bring your solutions into systems serving millions of customers alongside fast turnaround prototypes you own long term research bets the kind that reshape how amazon s fulfillment network operates at scale your work goes live about the team scot fulfillment optimization science tech fo snt is the applied research team behind amazon s fulfillment decision making systems we decide how orders get assigned to warehouses how capacity is allocated across the network and how cost and speed tradeoffs are managed in real time at global scale our models influence billions of euros in annual operational spend they protect sites from overload during peak reduce transportation costs and co2 emissions and ensure customers receive their packages when promised leadership relies on our science to make investment decisions worth hundreds of millions we are practitioners of large scale optimization mip formulations decomposition methods approximation algorithms and parallelisation we use machine learning where it sharpens our decisions including forecasting learned heuristics and multi armed bandits we pick the right tool for the problem not the fashionable one you will work alongside senior and principal scientists and collaborate with amazon scholars and academic partners who bring frontier research into our applied problems we code our prototypes to be production ready and collaborate with large engineering teams to ship systems not papers above all we have fun solving hard real world problems at real world speed failing learning and shipping along the way applied scientist customer360 us wa seattle what happens when you give ai the ability to remember not cached responses real structured memory that compounds over time and transfers across contexts we re building the science behind this and we need researchers who want to own the problem end to end this is a founding role on a new team you won t inherit models or maintain someone else s pipeline you ll define the research direction run experiments at scale and ship what works directly to production key job responsibilities as an applied scientist in our team you will be responsible for the research design and development of new ai technologies for knowledge acquisition and retrieval you will adopt or invent new machine learning and analytical techniques in the realm of information retrieval knowledge representation and large language models specific responsibilities include 1 design and implement novel approaches to knowledge extraction from heterogeneous unstructured data sources at organizational scale 2 build retrieval systems that match intent to relevant knowledge across domains solving the right memory at the right time problem 3 own the quality of memory generation what to capture how to structure it when to surface it and when to let it decay 4 run large scale experiments using amazon s compute infrastructure and massive real world datasets 5 develop evaluation frameworks for a system where quality means something new right knowledge right context right confidence level 6 collaborate with engineers to move from research prototype to production system in weeks not quarters 7 invent new approaches to temporal knowledge management how memories age conflict and compound over time 8 publish and patent novel approaches to knowledge acquisition and retrieval at top tier venues a day in the life you will solve real world problems by getting and analyzing large amounts of data generate insights and opportunities execute experiments and develop statistical and ml models the team is driven by business needs which requires collaboration with other scientists engineers and product managers across the organization you get to influence stakeholders with clear communication skills you innovate on behalf of the customer and strategically build features you will mentor junior members and help them grow about the team we re a new team within personalization focused on a different kind of recommendation not what product should this customer see but what knowledge should this ai use right now same scale same rigor entirely new problem space the science is at the intersection of information retrieval knowledge representation and llm reasoning and the right approach hasn t been established yet the team values innovation and offers a safe place to try fail and learn while fostering a culture of continuous improvement everyone is a leader and owner for everything we do as a team we offer creative space with an entrepreneurial work environment focusing on customer obsession applied scientist i amazon shipping in hr gurugram building large scale forecasting and optimization systems that power amazon s global transportation network and directly impact customer experience and cost key job responsibilities 1 guide model and system design across a range of techniques including tree based models deep learning lstms transformers llms and reinforcement learning 2 ensure models are production ready scalable and robust through close partnership with stakeholders 3 partner with product operations and engineering leaders to enable proactive decision making and corrective actions 4 own end to end business metrics directly influencing customer experience cost optimization and network reliability 5 help contribute to the broader ml community through publications conference submissions and internal knowledge sharing applied scientist prime video content reasoning enrichment localization us wa seattle 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 senior applied scientist real time conversational ai agi us ca sunnyvale we are looking for a senior applied scientist to help drive the research and development of real time multimodal conversational ai you will contribute across two focus areas advancing foundation models for speech and audio and building the post training systems reward modeling reinforcement learning that shape natural human like conversational behavior you will own a significant research area and contribute across the full model lifecycle from pre training and architecture design through post training alignment and real time deployment you will work at the frontier of what s possible in conversational ai with the compute data and runway to pursue problems that few teams in the world have the resources to tackle as a senior scientist you will drive the technical execution of your research area contribute to the team s roadmap and work closely with inference engineers to ensure your models are designed for real time production deployment key job responsibilities what you ll do foundation model scaling help build and train large scale multimodal foundation models for real time speech and audio generation from architecture design through production scale training advance the scaling and efficiency of conversational models including the relationship between data model size and real time performance design model architectures informed by hardware constraints and inference requirements working with inference engineers to ensure models are servable from inception develop training methodologies for multimodal models that jointly process and generate speech language and audio in real time streaming contexts contribute to the state of the art on efficient architectures and training methods for conversational ai at scale post training reinforcement learning design and build reward models and reward functions for speech systems capturing naturalness fluency conversational quality and real time responsiveness develop and apply reinforcement learning methods to shape conversational behavior teaching models natural timing responsiveness and fluid interaction build parts of the post training pipeline from sft through rl alignment optimized for real time multimodal outputs rather than text only generation design evaluation frameworks that capture the quality dimensions unique to real time conversation latency sensitivity audio quality prosody interaction naturalness real time perception generation advance the team s capabilities in real time perception the ability of the model to process incoming audio speech while simultaneously generating responses develop techniques for natural interactive systems where the model handles concurrent input and output with human like timing work at the intersection of model architecture and production constraints to ensure multimodal capabilities function within hard real time latency budgets applied scientist ii amazon supply chain us wa seattle as part of the aws applied ai solutions organization we have a vision to provide business applications leveraging amazon s unique experience and expertise that are used by millions of companies worldwide to manage day to day operations we will accomplish this by accelerating our customers businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges we blend vision with curiosity and amazon s real world experience to build opinionated turnkey solutions where customers prefer to buy over build we become their trusted partner with solutions that are no brainers to buy and easy to use we are looking for an applied scientist to join our team that is building enterprise applications leveraging machine learning generative ai and agentic ai to help millions of companies worldwide manage their day to day supply chain operations our mission is to accelerate our customers businesses through intuitive differentiated technology solutions that solve enduring supply chain challenges we blend strategic vision with curiosity and amazon s real world operational experience to build opinionated turnkey solutions that make the buy versus build decision a no brainer for our customers as an applied scientist you will design and develop machine learning models and algorithms that power intelligent supply chain applications at global scale you will work at the intersection of research and real world product impact translating scientific advances into production systems that serve millions of customers we operate like a startup within aws offering you...
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