Amazon

Senior Software Development Engineer to join the Gen AI & Strategic Initiatives team for Amazon Luna. The role involves architecting and building next-generation tools and platform infrastructure to support proprietary and third-party AI models, enabling Gen AI native games and enhancing internal team productivity through agents. Responsibilities include driving platform architecture, building scalable API infrastructure, launching cross-functional agents, optimizing performance, supporting developer experience, implementing observability, designing throttling systems, ensuring security and compliance, and driving operational excellence.

Senior Machine Learning Engineer role focused on developing and launching AI solutions for Prime Video's recommendation and personalization systems, utilizing deep learning, GenAI, and reinforcement learning. The role involves end-to-end ownership, experimental design, and collaboration with cross-functional teams to impact millions of customers.

MS or PhD student internship focused on research in machine learning, deep learning, generative AI, LLMs, speech, robotics, computer vision, optimization, OR, quantum computing, automated reasoning, or formal methods. The role involves designing and developing end-to-end systems, writing technical papers, creating roadmaps, and driving production-level projects. Experience with publications at top-tier conferences and solving business problems with ML/data mining/statistical algorithms is preferred.

This role focuses on enhancing the scalability, automation, and efficiency of large-scale training and real-time inference systems for Amazon Ads' Sponsored Products and Brands. The engineer will pioneer LLM inference infrastructure and work with applied scientists to optimize ML models and infrastructure, implementing end-to-end solutions. The team builds advanced ML models and infrastructure, from training to inference, including LLM-based systems, to deliver relevant ads.

Software Engineer role focused on building and optimizing the AWS Neuron compiler for custom AI chips (Inferentia and Trainium). The role involves transforming ML models (PyTorch, TensorFlow, JAX) into optimized code for these accelerators, with a focus on large language models and diffusion models. Requires strong software engineering skills, particularly in C++, and experience with compiler technologies is preferred.

Applied Scientist II role focused on designing and developing LLM-based machine learning solutions for language translation services at Amazon. The role involves applying expertise in LLMs, conducting data analysis, and evaluating new modeling techniques to improve translation accuracy and efficiency for millions of customers across 130+ locales. The team is leveraging Gen AI to build scalable solutions from scratch.

Manage a team of Applied Scientists, ML Engineers, and Software Development Engineers to develop science and engineering roadmaps for SPB ads CTR prediction using ML and Gen AI solutions. The role involves hiring, developing talent, and staying informed about scientific publications and industrial research trends. The team focuses on personalized shopping experiences through ML and GenAI solutions for response prediction and session-level understanding to optimize ad serving, targeting, sourcing, and bidding.

This role leads science and engineering for AI-powered sponsored product ads in offsite shopping experiences, focusing on generative AI and large-scale ML solutions. The goal is to revolutionize advertising by bridging human creativity with AI, impacting ad creation, optimization, performance analysis, and customer insights. The role involves extending campaigns to reach customers off-store and on third-party sites, working with external and internal partners, and driving results at scale with a GenAI-first approach. It requires significant experience in building large-scale ML/AI solutions and people management.

MS or PhD student internship in machine learning, deep learning, generative AI, LLMs, speech, robotics, computer vision, optimization, OR, quantum computing, automated reasoning, or formal methods. Focus on inventing, designing, and implementing state-of-the-art solutions for complex problems. Will own design and development of end-to-end systems, write technical white papers, create roadmaps, and drive production-level projects. Opportunity to design new algorithms and models, and deploy them into production, potentially publishing work at top-tier conferences.

MS or PhD student internship in machine learning, deep learning, generative AI, LLMs, speech, robotics, vision, optimization, OR, quantum computing, automated reasoning, or formal methods. Focus on designing and developing end-to-end systems, writing technical white papers, creating roadmaps, and driving production-level projects. Opportunity to design new algorithms and models, deploy solutions into production, and potentially publish work.

This role focuses on validating next-generation machine learning accelerators for AWS, covering the full vertical stack from silicon to system. The engineer will be responsible for developing validation strategies, executing test plans, debugging hardware and software, and collaborating with cross-functional teams to ensure the quality and performance of AI/ML accelerators used in AWS data centers.

The AGI team is seeking engineers to develop and maintain multi-modal and multi-lingual LLMs using scalable training and inference systems. The role involves deeply understanding technology landscapes, evaluating new technologies, and driving operational excellence. Key responsibilities include leading the design and automation of GenAI training compute infrastructure, mentoring engineers, identifying performance bottlenecks, and working with core AWS services, CI/CD pipelines, and Kubernetes.

Principal Applied Scientist role focused on building next-generation personalized shopping experiences at Amazon using LLMs, transformer models, and large-scale ranking systems. The role involves innovating features and models, leading science innovation, driving the science roadmap, and mentoring scientists. It aims to create a personalized shopping experience tailored to customer intent and product catalog understanding.

Applied Scientist role focused on building and deploying machine learning models for Amazon's consumer businesses. Responsibilities include analyzing large datasets, designing and evaluating scalable models, and collaborating with engineering teams for real-time implementation. The role emphasizes end-to-end ownership of business problems and optimizing operations through ML.

Senior Software Development Engineer at Amazon Annapurna Labs focused on leading a technical team to develop profiling and optimization tools for the Neuron ML accelerators fleet. The role involves working with hardware and software teams to identify bottlenecks and provide recommendations for improving performance of large ML workloads, including custom kernels.

The AWS Neuron team is seeking ML Compiler Engineers to optimize deep learning and GenAI workloads on AWS custom ML accelerators (Inferentia/Trainium). This role involves analyzing and optimizing system-level performance across the entire technology stack, from frameworks to runtime, and designing/implementing compiler optimizations. The position requires a passion for performance analysis, distributed systems, and machine learning, with a focus on improving the performance capabilities of the AWS Neuron SDK.

This role focuses on developing diverse datasets for training and evaluating AI models, utilizing methods like synthetic data generation, model-supported generation, and human-in-the-loop collections. The Language Engineer will collaborate with cross-functional teams to innovate and advance the state-of-the-art in AI model evaluation and training.

Senior Applied Science Manager to lead AI initiatives for seller growth at Amazon. The role involves developing scientific models for customer demand, selection identification, and prioritization signals. It also includes leading the development of next-generation agentic experiences for sellers, integrating insights into products like ABA and OX, and driving business impact through ML/LLM solutions.

Senior Systems Engineer for Amazon Prime Air's autonomous drone perception systems. This role involves translating operational requirements into system specifications, bridging ML algorithms with flight control, and ensuring perception systems meet aviation certification standards. The focus is on the integration and validation of ML-based perception for safe autonomous flight.

Research Scientist role focused on applying machine learning and data-driven solutions to optimize Amazon's last-mile delivery logistics. The role involves building ML models for business applications, developing scalable processes, and making strategic, data-driven decisions to improve customer experience and operational efficiency within the logistics network.

Software Development Manager to lead a team optimizing LLMs for inference on AWS custom accelerators (Neuron, Trainium, Inferentia). Focus on improving model enablement speed, experience, usability, and quality through features, infrastructure, tools, and automation. Requires strong background in LLM architectures, performance optimizations, and distributed inference.

Senior Machine Learning Engineer role at Amazon Prime Video focused on developing and launching AI solutions for recommendation and personalization systems. The role involves end-to-end ownership of ML models, including design, implementation, experimentation (A/B testing), and deployment for millions of customers. It requires experience with large-scale ML systems and recommendation systems.

Software Development Engineer focused on ML Systems within Amazon Annapurna Labs, working on AWS Neuron software for ML chips (Inferentia and Trainium). The role involves building and applying AI agents to accelerate customer adoption of this technology, optimizing performance, durability, cost, and security for AWS customers.

Senior ML Kernel Performance Engineer for AWS Neuron SDK, focusing on optimizing deep learning and GenAI workloads on custom ML accelerators (Inferentia, Trainium). The role involves designing and implementing high-performance compute kernels, optimizing performance at the hardware-software boundary, and collaborating with customers and internal teams on model enablement and acceleration.

The Annapurna Labs team at Amazon is seeking a Senior ML Kernel Performance Engineer to optimize deep learning and GenAI workloads on Amazon's custom ML accelerators (Inferentia and Trainium). This role involves crafting high-performance kernels, pushing the boundaries of AI acceleration at the hardware-software boundary, and collaborating with customers to enable their models. The engineer will work on compiler optimizations, performance analysis, and contribute to future architecture designs.

This role focuses on developing and launching end-to-end AI solutions for Prime Video's search and discovery systems, utilizing deep learning, GenAI, and reinforcement learning. The scientist will design and conduct experiments, collaborate with engineers and product managers, and publish research findings. The role is within the consumer domain, aiming to improve customer experience for millions of users.

Applied Scientist II at Amazon Prime Video focusing on personalization and discovery science. The role involves developing ML models for recommendation and search systems using deep learning, online learning, and optimization methods. It requires staying updated with the latest modeling techniques, publishing research findings, and applying advanced approaches like foundation models to solve cold-start problems and discover niche customer interests. The scientist will work on highly scalable page personalization solutions and collaborate with engineers and product managers.

Senior Machine Learning Compiler Engineer responsible for the ground-up development and scaling of a deep learning compiler stack for Amazon's ML accelerators (Inferentia and Trainium). The role involves architecting and implementing business-critical features, optimizing neural net models for custom hardware, and integrating with ML frameworks like PyTorch and TensorFlow.

Software Development Engineer III at Amazon Annapurna Labs, focusing on building and applying AI agents to simplify and accelerate customer adoption of AWS Neuron ML chips (Inferentia and Trainium). The role involves solving complex technical problems, designing and implementing innovative software solutions, and working with external and internal customers to identify adoption obstacles and opportunities in the Generative AI space.

This role is for a Sr. Machine Learning Compiler Engineer III on the AWS Neuron team, focusing on the development and scaling of a compiler for ML accelerators. The role involves architecting and implementing features for a deep learning compiler stack that optimizes neural network performance on custom AWS hardware, integrating with frameworks like PyTorch and TensorFlow. The goal is to provide significant performance improvements for large-scale ML workloads.

Senior Software Development Engineer to join Ring's AI Team, focusing on cloud services for machine learning operation pipelines that handle large-scale data and enable rapid model optimization. The role involves building and scaling platforms for AI model development and deployment, collaborating with cross-functional teams, and ensuring the delivery of robust backend systems.

Senior Software Development Engineer to join Ring's AI Team, focusing on cloud services for machine learning operation pipelines that handle large-scale data and enable rapid model optimization. The role involves building and scaling platforms for AI model development and deployment, collaborating with cross-functional teams, and ensuring the delivery of robust backend systems.

Sr. Applied Scientist role focused on developing AI prototypes and concepts for the JP Manga business, involving research, design, and training/tuning of NLP and Computer Vision models for applications like translation, summarization, extraction, boundary detection, image understanding, and generation. The role emphasizes tangible business impact and collaboration with product managers and engineers, with opportunities for publication.

Senior Software Development Engineer focused on Generative AI within Amazon's Annapurna Labs, specifically working with the Neuron SDK and ML chips (Inferentia and Trainium). The role involves building and applying AI agents to improve customer adoption of these chips, optimizing software solutions for performance, durability, cost, and security, and collaborating with cross-functional teams including compiler, hardware, and ML engineers. Experience in the Generative AI space is a hard requirement.

Data Scientist role focused on developing and implementing statistical, causal, and machine learning techniques for forecasting and inventory management within Amazon's retail supply chain. The role involves creating prototypes, collaborating with software teams for production implementation, and analyzing key business metrics to influence business direction.

Senior Applied Scientist role at Amazon Advertising focused on building and deploying end-to-end machine learning models to improve traffic monetization and merchandise sales. The role involves leading ML efforts, performing hands-on analysis, driving ambiguous projects, and establishing scalable processes for model development and deployment.

Software Development Engineer role on the AWS Solutions team, focusing on building and scaling the Machine Learning platform for Just Walk Out (JWO) Technology. The role involves developing algorithms for computer vision, image recognition, and machine learning within a distributed systems environment, with a focus on scaling ML platforms.

Senior Manager for AI Inference Technology, leading a team to build fundamental inference technology building blocks and libraries for AWS Neuron SDK, optimizing models for Trainium and Inferentia devices. Focuses on the full development life cycle of inference libraries, enabling customers to optimize LLMs, multimodal, and generative models.

Manager III leading a team of compiler engineers to develop, deploy, and scale a compiler targeting AWS Inferentia and Trainium ML accelerators. The role involves technical leadership, innovation, and collaboration with AWS ML services teams to ensure the Neuron SDK meets customer needs for high performance, low cost, and ease of use. Deep knowledge of resource management, scheduling, code generation, and optimization is required.

The role focuses on optimizing the performance of machine learning kernels for AWS's custom ML accelerators (Inferentia and Trainium) by developing and implementing high-performance compute kernels, optimizing compiler optimizations, and analyzing kernel-level performance. This involves working at the hardware-software boundary to ensure optimal performance for deep learning and GenAI workloads.

Senior Applied Scientist role at Amazon Prime Video focusing on Playback Intelligence. The role involves applying machine learning and data science to optimize video streaming quality, detect anomalies, and leverage LLMs and generative AI. Responsibilities include end-to-end ownership of product and user experience, translating business requirements into ML deliverables, defining research directions, conducting experiments, and mentoring junior scientists. The role requires experience in building ML models for business applications and designing AI solutions for real-world use cases.

Senior Software Development Engineer focused on pre-silicon hardware/software co-development for next-generation machine learning chips (like Trainium) used in AWS. The role involves working with architecture, design, and emulation teams, writing bare-metal software and ML workloads to verify chip functionality and performance.

Principal Applied Scientist role focused on applying machine learning, optimization, and economics to improve Amazon's Stores business, specifically in areas like delivery speed, seller fees, and LLM applications. The role involves leading a team, developing scientific models, benchmarks, and services, and deploying solutions in partnership with product teams.