How Amazon scientists are driving success for Alexa in the car

From noisy cars to unreliable signals, researchers have worked to extend the Alexa experience to vehicles on the move.

“Alexa, where’s the nearest coffee shop?”

In vehicles with Alexa, drivers can pose questions like that — while keeping their eyes on the road and hands on the wheel. Amazon’s cloud-based voice assistant technology works with the car’s navigation system to figure out where the nearest coffee shop is and guide drivers to it.

AlexaAuto_V2_GIF (1).gif
Alexa’s local voice control of native features in the car such as adjusting the air conditioning, tuning the radio, opening the sunroof, and turning on the reading light do not require a connection to the cloud.

Interactions like this hinge on the ability of the built-in hardware and software to accurately detect when Alexa is invoked.

“We want to take the Alexa experience that customers are familiar and comfortable with in the home and extend that experience to cars,” said Jinghao Liu, a principal hardware engineer with Amazon Smart Vehicles at the team’s vehicle lab in San Jose, California.

The challenge is that cars are a different acoustic and operating environment than a home. They are noisier, for starters. What’s more, the bulk of customer needs in a car are location-specific and tied to real-time road, traffic, and weather conditions. And, as most drivers know, cellular service in a car is never 100% reliable, especially outside of cities and towns.

Related content
Dataset that requires question-answering models to look up multiple facts and perform comparisons bridges a significant gap in the field.

There’s another complication: the hardware. Amazon scientists often work with car manufacturers on the design and implementation of their systems, but during validation in the 6,000-square-foot vehicle lab, there is one element they cannot control for.

“We are giving our partners access to Alexa services and functionalities, but we don’t make the device,” explained Parimal Tathavadekar, a senior manager on the Amazon Smart Vehicles team. “We make sure that the integration of our technology with their device is correct and happily working.”

Tackling road noise

Perhaps the biggest difference between speaking to Alexa in a car versus at home is the increase in noise, Liu noted. Think of the rumble from tires on the road and turbulence from the wind, as well as whatever radio station, podcast, or music is playing through the stereo.

To invoke Alexa, the car’s microphones need to pick out the speaker’s voice from this din and send the voice signal to the automatic speech recognition model running in the cloud.

AlexaAuto_V1_GIF.gif
Inside Amazon’s vehicle lab, engineers simulate vehicle locations, validating that Alexa is integrated with the car’s navigation system and able to provide relevant information and experiences to customers on the go.

In design meetings, Amazon scientists collaborate with car manufacturers on techniques that allow the car’s built-in system to focus on the person speaking to Alexa and cancel out acoustic echo and background noise.

For example, Amazon scientists recommend car manufacturers use a signal-processing technique called beamforming that steers microphone arrays toward a voice signal coming from a particular source, such as the driver, while suppressing audio interference from all other directions.

“You are laser focusing on just that one angle,” Liu explained, adding that beamforming is used in the Amazon Echo family of devices.

The science behind Alexa
Combining psychoacoustics, signal processing, and speaker beamforming enhances stereo audio and delivers an immersive sound experience for customers.

Another recommended technique called acoustic echo cancellation blocks out the noise from the car’s speakers so that music played by the speaker does not interfere with the customer requests. Manufacturers can also deploy various noise reduction algorithms, which learn to separate a speaker’s voice from other background noise.

Inside the vehicle lab, Amazon scientists use software and acoustic tools to simulate road noise, music, and other acoustic elements as they test and validate Alexa integration with the car manufacturer’s built-in systems.

To achieve certification, the built-in system must stream a voice signal to the automatic-speech-recognition model that’s of a similar quality to the voice signals streamed from any of the other hundreds of millions of Alexa-connected devices around the world.

“This model is serving millions of devices at home and on the go, so we make sure that the received audio signal is reasonable, and our cloud takes care of the rest,” Liu said.

Personal mobility

Customer interactions with Alexa in cars are often location-specific, ranging from queries about points of interest along the way to driving directions that avoid traffic jams.

Amazon does not have its own navigation system or a proprietary database of points of interest, noted Tathavadekar.

Alexa in space
From physical constraints to acoustic challenges, learn how Amazon collaborated with NASA and Lockheed Martin to get Alexa to work in space.

Rather, Alexa AI is designed to resolve a speaker’s query and hand off such intents to the car’s navigation engine for geolocation information. Alexa uses this data to interact with third-party databases for information about points of interest.

The use of Alexa in conjunction with a car’s native navigation system enables drivers to accomplish tasks via voice instead of “having to type or touch through different navigation screens, which can be a distraction,” Tathavadekar said.

The contextual awareness also allows Alexa to deliver experiences such as tuning in to local radio stations on a cross-country road trip.

Inside Amazon’s vehicle lab, engineers simulate vehicle locations, Liu explained, validating that Alexa is integrated with the car’s navigation system and able to provide relevant information and experiences to customers on the go.

Staying connected

Quality of connection is another factor in how quickly a device can connect to Alexa and get a response.

“When you’re driving, you’re going to go through a patch with poor reception,” Liu noted. That’s why he and his colleagues also test how built-in systems handle scenarios with poor, or no, cellular signal.

In today’s world, customers are used to getting a quick response from Alexa at home, Liu noted. If the response takes longer on the road, the customer may think the device is defective. That’s why Amazon makes sure that Alexa stays connected to customers, even when the cellular signal is lost.

For example, Alexa’s local voice control of native features in the car such as adjusting the air conditioning, tuning the radio, opening the sunroof, and turning on the reading light do not require a connection to the cloud.

In the vehicle lab, the team measures the built-in hardware device’s latency with various levels of connectivity. If the cellular signal strength is strong, but Alexa is slow to respond, that may be an indication that the hardware needs to be re-engineered.

As connectivity becomes ubiquitous, Liu added, the latency expectations will continue to tighten. Amazon scientists also expect that experiences within and outside of cars will become more engaging and complex going forward.

“Every year, we’re adding new features and equipment to our test arsenal,” Liu said.

Making it personal

The Alexa experience in a car should also be as personal as the interactions that customers have with Alexa in their homes, noted Tathavadekar. That’s possible because cars with built-in Alexa are just another endpoint for individual Alexa accounts, which are stored in the cloud.

“We are able to render experiences on all the endpoints where that particular person might be operating,” he said. “You might create a playlist at home and play it in your car.”

In a similar fashion, a driver in their car can ask Alexa to add an item to a shopping list that they started in the kitchen or ask Alexa to lock the front door of a connected smart home.

Providing that personal and familiar experience with Alexa in cars requires a system that seamlessly extends the Alexa experience from an Echo at home to a vehicle that might be traveling down a congested highway with patchy connectivity.

“The big chunk of our work is to collaborate with the major car manufacturers to create unique experiences that delight car owners,” said Liu. “They are our partners who help deliver the product to customers. They’re making the car, and we add this piece of personalized voice assistant into the digital experience.”

Research areas

Related content

US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, CA, Palo Alto
The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through state-of-the-art generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond! Key job responsibilities This role will be pivotal in redesigning how ads contribute to a personalized, relevant, and inspirational shopping experience, with the customer value proposition at the forefront. Key responsibilities include, but are not limited to: - Contribute to the design and development of GenAI, deep learning, multi-objective optimization and/or reinforcement learning empowered solutions to transform ad retrieval, auctions, whole-page relevance, and/or bespoke shopping experiences. - Collaborate cross-functionally with other scientists, engineers, and product managers to bring scalable, production-ready science solutions to life. - Stay abreast of industry trends in GenAI, LLMs, and related disciplines, bringing fresh and innovative concepts, ideas, and prototypes to the organization. - Contribute to the enhancement of team’s scientific and technical rigor by identifying and implementing best-in-class algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling. - Mentor and grow junior scientists and engineers, cultivating a high-performing, collaborative, and intellectually curious team. A day in the life As an Applied Scientist on the Sponsored Products and Brands Off-Search team, you will contribute to the development in Generative AI (GenAI) and Large Language Models (LLMs) to revolutionize our advertising flow, backend optimization, and frontend shopping experiences. This is a rare opportunity to redefine how ads are retrieved, allocated, and/or experienced—elevating them into personalized, contextually aware, and inspiring components of the customer journey. You will have the opportunity to fundamentally transform areas such as ad retrieval, ad allocation, whole-page relevance, and differentiated recommendations through the lens of GenAI. By building novel generative models grounded in both Amazon’s rich data and the world’s collective knowledge, your work will shape how customers engage with ads, discover products, and make purchasing decisions. If you are passionate about applying frontier AI to real-world problems with massive scale and impact, this is your opportunity to define the next chapter of advertising science. About the team The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond!
US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
US, WA, Bellevue
We are seeking a passionate, talented, and inventive individual to join the Applied AI team and help build industry-leading technologies that customers will love. This team offers a unique opportunity to make a significant impact on the customer experience and contribute to the design, architecture, and implementation of a cutting-edge product. The mission of the Applied AI team is to enable organizations within Worldwide Amazon.com Stores to accelerate the adoption of AI technologies across various parts of our business. We are looking for a Senior Applied Scientist to join our Applied AI team to work on LLM-based solutions. On our team you will push the boundaries of ML and Generative AI techniques to scale the inputs for hundreds of billions of dollars of annual revenue for our eCommerce business. If you have a passion for AI technologies, a drive to innovate and a desire to make a meaningful impact, we invite you to become a valued member of our team. You will be responsible for developing and maintaining the systems and tools that enable us to accelerate knowledge operations and work in the intersection of Science and Engineering. You will push the boundaries of ML and Generative AI techniques to scale the inputs for hundreds of billions of dollars of annual revenue for our eCommerce business. If you have a passion for AI technologies, a drive to innovate and a desire to make a meaningful impact, we invite you to become a valued member of our team. We are seeking an experienced Scientist who combines superb technical, research, analytical and leadership capabilities with a demonstrated ability to get the right things done quickly and effectively. This person must be comfortable working with a team of top-notch developers and collaborating with our research teams. We’re looking for someone who innovates, and loves solving hard problems. You will be expected to have an established background in building highly scalable systems and system design, excellent project management skills, great communication skills, and a motivation to achieve results in a fast-paced environment. You should be somebody who enjoys working on complex problems, is customer-centric, and feels strongly about building good software as well as making that software achieve its operational goals.
IN, KA, Bengaluru
Do you want to lead the development of advanced machine learning systems that protect millions of customers and power a trusted global eCommerce experience? Are you passionate about modeling terabytes of data, solving highly ambiguous fraud and risk challenges, and driving step-change improvements through scientific innovation? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right place for you. We are seeking a Senior Applied Scientist to define and drive the scientific direction of large-scale risk management systems that safeguard millions of transactions every day. In this role, you will lead the design and deployment of advanced machine learning solutions, influence cross-team technical strategy, and leverage emerging technologies—including Generative AI and LLMs—to build next-generation risk prevention platforms. Key job responsibilities Lead the end-to-end scientific strategy for large-scale fraud and risk modeling initiatives Define problem statements, success metrics, and long-term modeling roadmaps in partnership with business and engineering leaders Design, develop, and deploy highly scalable machine learning systems in real-time production environments Drive innovation using advanced ML, deep learning, and GenAI/LLM technologies to automate and transform risk evaluation Influence system architecture and partner with engineering teams to ensure robust, scalable implementations Establish best practices for experimentation, model validation, monitoring, and lifecycle management Mentor and raise the technical bar for junior scientists through reviews, technical guidance, and thought leadership Communicate complex scientific insights clearly to senior leadership and cross-functional stakeholders Identify emerging scientific trends and translate them into impactful production solutions
GB, London
We are looking for a Senior Economist to work on exciting and challenging business problems related to Amazon Retail’s worldwide product assortment. You will build innovative solutions based on econometrics, machine learning, and experimentation. You will be part of a interdisciplinary team of economists, product managers, engineers, and scientists, and your work will influence finance and business decisions affecting Amazon’s vast product assortment globally. If you have an entrepreneurial spirit, you know how to deliver results fast, and you have a deeply quantitative, highly innovative approach to solving problems, and long for the opportunity to build pioneering solutions to challenging problems, we want to talk to you. Key job responsibilities * Work on a challenging problem that has the potential to significantly impact Amazon’s business position * Develop econometric models and experiments to measure the customer and financial impact of Amazon’s product assortment * Collaborate with other scientists at Amazon to deliver measurable progress and change * Influence business leaders based on empirical findings
IN, KA, Bengaluru
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning and Data Sciences team for India Consumer Businesses. If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you. Major responsibilities - Use machine learning and analytical techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes - Design, development, evaluate and deploy innovative and highly scalable models for predictive learning - Research and implement novel machine learning and statistical approaches - Work closely with software engineering teams to drive real-time model implementations and new feature creations - Work closely with business owners and operations staff to optimize various business operations - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Mentor other scientists and engineers in the use of ML techniques Key job responsibilities Use machine learning and analytical techniques to create scalable solutions for business problems Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes Design, develop, evaluate and deploy, innovative and highly scalable ML models Work closely with software engineering teams to drive real-time model implementations Work closely with business partners to identify problems and propose machine learning solutions Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model maintenance Work proactively with engineering teams and product managers to evangelize new algorithms and drive the implementation of large-scale complex ML models in production Leading projects and mentoring other scientists, engineers in the use of ML techniques About the team International Machine Learning Team is responsible for building novel ML solutions that attack India first (and other Emerging Markets across MENA and LatAm) problems and impact the bottom-line and top-line of India business. Learn more about our team from https://www.amazon.science/working-at-amazon/how-rajeev-rastogis-machine-learning-team-in-india-develops-innovations-for-customers-worldwide
US, MA, N.reading
Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. As a Senior Applied Scientist in Sensing, you will develop innovative and complex sensing systems for our emerging robotic solutions and improve existing on-robot sensing to optimize performance and enhance customer experience. The ideal candidate has demonstrated experience designing and troubleshooting custom sensor systems from the ground up. They enjoy analytical problem solving and possess practical knowledge of robotic design, fabrication, assembly, and rapid prototyping. They thrive in an interdisciplinary environment and have led the development of complex sensing systems. Key job responsibilities - Design and adapt holistic on-robot sensing solutions for ambiguous problems with fluid requirements - Mentor and develop junior engineers - Work with an interdisciplinary team to execute product designs from concept to production including specification, design, prototyping, validation and testing - Own the detailed design and performance of a sensing system design - Work with the Operations, Manufacturing, Supply Chain and Quality organizations as well as vendors to ensure a smooth transition of concept to product - Write functional specifications, design verification plans, and functional test procedures - Exhibit role model behaviors of applied science best practices, thorough and predictive analysis and cradle to grave ownership About the team 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!