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 ROLEThe playback team at Audible owns the core playback logic end-to-end. All our services, systems and players are invoked each time customers click play (or download a title for offline playback) to enjoy the best playback experience. Specifically we own 1) Core playback logic on players such as Android, iOS and Web, 2) Playback services related to content delivery infrastructure, security & digital rights management, and processing / synchronization of listening data across devices, 3) listening statistics that power royalties, returns, creator metrics, and more, 4) Playback metrics, insights and platform, obsessing over the core playback metrics and trends that represent the service health of Audible Playback and delivery, 5) Unified Audible Playback SDK, that can be shared with both 3P and 1P customers, 6) Player optimizations related to QoE, ABR and Quality/CDN selection across Android, iOS and web, Server optimizations to segment the customer data and tune playback parameters in real-time, 7) all of the efforts around quality of experience monitoring which enable timely, accurate and automated detection of QoS performance degradations and identify opportunities to improve playback experience, 8) automated ML based review and sentiment analysis system that will proactively analyze posts across social media feeds, customer reviews to automatically establish relational models to identify and cross correlate issues detected by tools such as Anomaly detection, and finally, 9) Driving Audible's playback initiatives to establish faculty collaborations to advance state of the art in Internet Audio.ABOUT YOUAs an applied scientist on our team, you will wear many hats and work in a highly collaborative environment that's more startup than big company. You'll need to tackle complex problems that span a variety of domains: Machine learning, Artificial intelligence, Natural Language processing, real-time and distributed systems and help us build services and systems from the ground up which scale and serve billions of requests per day, with obsessively high reliability and low operational overhead. You'll have an opportunity to explore, innovate, invent and simplify various Playback state-of-art services and algorithms which leverage both custom and Industry proven Machine Learning, Natural Language processing and Artificial Intelligence technologies. Your work will focus on training and evaluating models and deploying them to production where we continuously monitor and evaluate. You'll work on large engineering efforts that solve significantly complex problems facing global customers. You'll be trusted to operate with independence and are often assigned to focus on areas with significant impact on audience satisfaction. You must be equally comfortable digging in to customer requirements as you are drilling into design with development teams and developing production ready learning models. You consistently bring strong, data-driven business and technical judgment to decisions. We experiment a lot and it is a must to learn and be curios. You'll be encouraged to see the big picture, be innovative, and positively impact millions of customers.As a Senior Applied Scientist, you will...* Understand large complex use cases across the Playback org and design scalable, efficient, and automated solutions* Design, develop, and deploy state-of-art Optimization services & algorithms and novel adaptive bitrate algorithms* Partner closely with other Amazon scientists focused on streaming insights and optimization use cases* Work closely with teams of scientists and software engineers to drive real-time model implementations and deliver novel and highly impactful features* Push the boundary of innovation* Mentor and grow the scientists in the team and across AmazonABOUT AUDIBLEAudible 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.

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 In this role, you'll employ scalable cutting-edge machine learning (ML), deep learning (DL), and Natural Language Processing (NLP) techniques to detect and predict fraudulent activities, enhance fraud investigation capabilities, and develop advanced fraud protection and defense mechanisms. You'll leverage these technologies to analyze complex patterns in transaction data, identify anomalies, and create predictive models that can anticipate potential fraud before it occurs. Your work will be crucial in safeguarding the company's assets, protecting customers from financial harm, and maintaining the integrity of our systems. You'll translate intricate fraud patterns into actionable insights, enabling rapid response to emerging threats and informing critical business decisions related to risk management. You'll operate in an agile environment in which we own and collaborate on the life cycle of research, design, and model development of relevant projects. As an Applied Scientist, you will... - Protect Audible's customers and content creators against the onslaught of AI-generated fraud - Develop Amazon-scale data engineering & modeling pipelines - Imagine and invent before the business asks, and create groundbreaking fraud detection and mitigation solutions using cutting-edge approaches - Work closely with other data scientists, ML experts, engineers as well as business across the globe, and on cross-disciplinary efforts with other scientists within Amazon - Contribute to the growth of the Audible Data Science team by sharing your ideas, intellectual property and learning from others 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.

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 ROLEIn this role, you'll employ scalable cutting-edge machine learning (ML), deep learning (DL), and Natural Language Processing (NLP) techniques to detect and predict fraudulent activities, enhance fraud investigation capabilities, and develop advanced fraud protection and defense mechanisms. You'll leverage these technologies to analyze complex patterns in transaction data, identify anomalies, and create predictive models that can anticipate potential fraud before it occurs. Your work will be crucial in safeguarding the company's assets, protecting customers from financial harm, and maintaining the integrity of our systems. You'll translate intricate fraud patterns into actionable insights, enabling rapid response to emerging threats and informing critical business decisions related to risk management. You'll operate in an agile environment in which we own and collaborate on the life cycle of research, design, and model development of relevant projects. As an Applied Scientist, you will...<ol><li>Protect Audible’s customers and content creators against the onslaught of AI-generated fraud.</li><li>Develop Amazon-scale data engineering & modeling pipelines.</li><li>Imagine and invent before the business asks, and create groundbreaking fraud detection and mitigation solutions using cutting-edge approaches.</li><li>Work closely with other data scientists, ML experts, engineers as well as business across the globe, and on cross-disciplinary efforts with other scientists within Amazon.</li><li>Contribute to the growth of the Audible Data Science team by sharing your ideas, intellectual property and learning from others.</li></ol> ABOUT AUDIBLEAudible 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. Minimum Qualifications:<ul><li>MS in one of the following disciplines: Computer Science, Statistics, Data Science, Economics, Applied Math, Operational Research or a related quantitative field +5 yrs relevant experience; or PhD.</li><li>Fluency in Python, SQL or similar scripting languages and skilled at Java, C++, or other programming languages.</li><li>Experience in algorithm development.</li><li>Depth and breadth in state-of-the-art machine learning technologies.</li><li>Machine Learning Pipeline orchestration with AWS (SageMaker, Batch, Lambda, Step Functions) or similar cloud platforms.</li><li>Big Data Engineering with Spark / AWS EMR & Glue.</li></ul>
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Posting TypeHybrid, RemoteJob OverviewEvery year, the global justice system benefits from the insights of Relativity AI on billions of documents- and we are just getting started on our journey to use AI to improve each user experience, product, legal matter, and investigation at Relativity. We are focused on helping our users discover the truth more quickly, organize their data, and act on it with confidence.Relativity has established a set of AI Principles to guide product development and underscore their commitment to developing responsible AI. These principles include building AI with purpose that delivers value for customers, empowering customers with clarity and control, ensuring fairness in AI development, championing privacy, placing the security of customers' data at the heart of everything they do, and acting with a high standard of accountability.As a team, we believe in exploration, experimentation, and bringing your curiosity to work every day. We know that you can't innovate without experimentation - and a little failure happens on the path to invention. We use the latest and greatest to ensure we are the best. We experiment, ship, and learn every day.About The AI Team At RelativityRelativity is heavily invested in developing new AI products and features for our customers. Our team spans across many teams and functions across Relativity Engineering, including our data engineering team, ML Ops team, applied science team, and product-focused feature teams for our RelOne AI suite of products.About The Senior Applied Scientist, Generative AI RoleThe Senior Applied Scientist, Generative AI will develop generative and machine-learning products for the eDiscovery industry in close collaboration with their applied science, product, design, and engineering team. You will use your experience in applied science to build models that help legal professionals organize data, discover the truth, and act on it. Relativity is the recognized leader of AI in eDiscovery, as well as the builder of the market's leading product, Relativity One. As a Senior Applied Scientist, you will contribute to our balanced portfolio of research, development, and operation of transformational generative AI technologies according to the responsible AI approach laid out by our AI Principles.Job Description and RequirementsResponsibilities<ul><li>Develop machine-learning models and algorithms to be released as new product features.</li><li>Collaborate with engineers to develop production-level code and contribute to the entire model deployment lifecycle.</li><li>Contribute to developing internal standards, processes and tooling for leveraging generative AI.</li><li>Collaborate with Product to assemble and synthesize datasets for evaluation and model-building.</li></ul>Minimum Qualifications<ul><li>A Master's Degree in Computer Science, or a quantitative discipline, AND 2 years of relevant industry experience OR a Ph.D. in Computer Science or a quantitative discipline OR the equivalent of 5 years of relevant academic/industry experience.</li><li>Experience in building search solutions at the scale of hundreds of millions of documents.</li><li>Experience with generative AI models and leveraging them as part of more complex systems to solve domain-specific tasks.</li><li>Experience in the full model development lifecycle.</li><li>Legal industry experience, or interest in the legal industry and justice system.</li></ul>Preferred Qualifications<ul><li>Experience in building and deploying models leveraging large language models.</li><li>Proficient in programming and scripting in a language such as Python.</li><li>Experience in Kubernetes container-based development tooling and processes.</li><li>Comfortable with command-line tools in UNIX environments.</li><li>Intentional communicator - uses inclusive language whenever possible, communicates data science concepts to colleagues of all backgrounds, uses technical vocabulary judiciously and with intention.</li></ul>Relativity is committed to competitive, fair, and equitable compensation practices.This position is eligible for total compensation which includes a competitive base salary, an annual performance bonus, and long-term incentives.The expected salary range for this role is between $146,000 and $218,000. The final offered salary will be based on several factors, including but not limited to the candidate's depth of experience, skill set, qualifications, and internal pay equity. Hiring at the top end of the range would not be typical, to allow for future meaningful salary growth in this position.
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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 ROLEThe playback team at Audible owns the core playback logic end-to-end. All our services, systems and players are invoked each time customers click play (or download a title for offline playback) to enjoy the best playback experience. Specifically we own:<ol><li>Core playback logic on players such as Android, iOS and Web</li><li>Playback services related to content delivery infrastructure, security & digital rights management, and processing / synchronization of listening data across devices</li><li>Listening statistics that power royalties, returns, creator metrics, and more</li><li>Playback metrics, insights and platform, obsessing over the core playback metrics and trends that represent the service health of Audible Playback and delivery</li><li>Unified Audible Playback SDK, that can be shared with both 3P and 1P customers</li><li>Player optimizations related to QoE, ABR and Quality/CDN selection across Android, iOS and web</li><li>Server optimizations to segment the customer data and tune playback parameters in real-time</li><li>Quality of experience monitoring which enables timely, accurate and automated detection of QoS performance degradations and identifies opportunities to improve playback experience</li><li>Automated ML based review and sentiment analysis system that will proactively analyze posts across social media feeds, customer reviews to automatically establish relational models to identify and cross correlate issues detected by tools such as Anomaly detection</li><li>Driving Audible’s playback initiatives to establish faculty collaborations to advance state of the art in Internet Audio</li></ol> ABOUT YOUAs an applied scientist on our team, you will wear many hats and work in a highly collaborative environment that’s more startup than big company. You'll need to tackle complex problems that span a variety of domains: Machine learning, Artificial intelligence, Natural Language processing, real-time and distributed systems and help us build services and systems from the ground up which scale and serve billions of requests per day, with obsessively high reliability and low operational overhead. You'll have an opportunity to explore, innovate, invent and simplify various Playback state-of-art services and algorithms which leverage both custom and Industry proven Machine Learning, Natural Language processing and Artificial Intelligence technologies. Your work will focus on training and evaluating models and deploying them to production where we continuously monitor and evaluate. You'll work on large engineering efforts that solve significantly complex problems facing global customers. You'll be trusted to operate with independence and are often assigned to focus on areas with significant impact on audience satisfaction. You must be equally comfortable digging in to customer requirements as you are drilling into design with development teams and developing production ready learning models. You consistently bring strong, data-driven business and technical judgment to decisions. We experiment a lot and it is a must to learn and be curious. You'll be encouraged to see the big picture, be innovative, and positively impact millions of customers. As a Senior Applied Scientist, you will...<ul><li>Understand large complex use cases across the Playback org and design scalable, efficient, and automated solutions</li><li>Design, develop, and deploy state-of-art Optimization services & algorithms and novel adaptive bitrate algorithms</li><li>Partner closely with other Amazon scientists focused on streaming insights and optimization use cases</li><li>Work closely with teams of scientists and software engineers to drive real-time model implementations and deliver novel and highly impactful features</li><li>Push the boundary of innovation</li><li>Mentor and grow the scientists in the team and across Amazon</li></ul> ABOUT AUDIBLEAudible 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. Minimum Requirements:<ul><li>5+ years of industry experience in Deep learning, Natural Language Processing/Understanding, Reinforcement learning and Speech processing</li><li>PhD in one of the following disciplines: Computer Science, Machine Learning, Statistics, Data Science, Applied Math, Operational Research or a related quantitative field</li><li>Fluency in Python, SQL or similar scripting languages and skilled at Java, C++, or other programming languages</li><li>Experience in algorithm development</li><li>Depth and breadth in state-of-the-art machine learning technologies</li><li>Machine Learning Pipeline orchestration with AWS (SageMaker, Batch, Lambda, Step Functions) or similar cloud-platforms</li><li>Big Data Engineering with Spark / AWS EMR & Glue</li></ul>
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Applied Scientist Jobs in Newark, NJ

Senior Applied Scientist
December 14, 2024

Applied Scientist
December 12, 2024

Senior Applied Scientist
December 11, 2024

Applied Scientist Job Id 2809772
December 10, 2024

Senior Applied Scientist
December 8, 2024

Senior Applied Scientist Job Id 2809770
December 7, 2024

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Applied Scientist Jobs in Newark, NJ

Senior Applied ScientistDecember 14, 2024

Applied ScientistDecember 12, 2024

Senior Applied ScientistDecember 11, 2024

Applied Scientist Job Id 2809772December 10, 2024

Senior Applied ScientistDecember 8, 2024

Senior Applied Scientist Job Id 2809770December 7, 2024