Amazon

Software Development Engineer II role focused on building and optimizing GenAI serving systems and ML platforms at massive scale. The role involves working with LLMs, VLMs, and multimodal foundation models, including optimized model serving, distillation, quantization, distributed inference, vector indices, and agentic systems. The primary focus is on the engineering and infrastructure aspects of bringing AI models to production, with a secondary involvement in agentic systems.

Senior Applied Scientist role focused on building agentic AI products for businesses, involving end-to-end GenAI project ownership, ML model development and deployment, and research into innovative ML approaches. The role emphasizes building multi-agent systems using techniques like fine-tuning and reinforcement learning, with a focus on customer-facing features and scalable solutions.

Machine Learning Scientist role focused on Generative AI within AWS, aiming to identify customer needs and improve cloud adoption. The role involves building Agentic AI systems, fine-tuning LLMs, applying Reinforcement Learning, and generating insights from large datasets, with a focus on taking ideas from conception to production.

Solutions Architect for AWS Telco customers, focusing on customer experience transformation using AI agents, conversational AI, and omnichannel orchestration. The role involves designing and building solutions with AWS services like Amazon Connect and Bedrock AgentCore, driving adoption, and influencing product roadmaps.

Senior Software Development Engineer for AWS Neuron, focusing on accelerating deep learning and GenAI workloads on Amazon's custom ML accelerators (Inferentia and Trainium). The role involves designing, developing, and optimizing ML models and frameworks for deployment, with a strong emphasis on distributed inference, performance tuning (latency and throughput), and system-level optimizations for LLMs.

Data Scientist to build and launch production-grade agentic capabilities for Amazon Seller Assistant, a multi-agent GenAI system. Responsibilities include analyzing seller pain points, designing measurement frameworks, applying NLP and statistical modeling, and collaborating with cross-functional teams to improve the seller experience at Amazon's scale.

Applied Scientist role focused on building LLM-driven conversational assistants for enterprise use cases in hospitality and senior living, leveraging Amazon's scale and data. Responsibilities include developing core LLM technologies, prompt optimization, and building/measuring metrics for these systems.

Senior Software Development Engineer focused on building and scaling foundational LLMs for Amazon Stores. The role involves architecting and building ML infrastructure for LLM training and post-training workflows (fine-tuning, RL, continuous learning), transforming customer interactions into training signals, optimizing RL systems, and partnering with scientists to productionize frontier techniques like RLHF and agentic workflows. Emphasis on end-to-end system ownership, including design, implementation, deployment, and observability, with a focus on low-level optimization like CUDA kernels and ML platforms.

Senior Applied Scientist role focused on AI-driven structured field robotics, including path planning, fleet coordination, and control systems. The role involves leading research, translating breakthroughs into production solutions at scale, and owning end-to-end delivery of algorithmic solutions. It requires a PhD or Master's with significant experience in robotics, ML, and algorithm development, with a focus on publishing research and mentoring junior scientists. The team operates at the intersection of planning, algorithmic, and ML research with production systems.

Software Development Engineer role focused on building and launching production-grade, multi-agent GenAI systems for Amazon Seller Assistant. The role involves end-to-end ownership from customer insight to shipped product, operating at Amazon's scale.

Applied Scientist role focused on leveraging computer vision, generative AI, and deep learning to enhance vehicle navigation and ensure safe, efficient deliveries by analyzing multimodal data. The role involves building large-scale ML systems, translating business requirements into prototypes, and optimizing models for production and edge devices.

The Applied Scientist II role focuses on developing and improving machine learning systems for industrial robotics, specifically leveraging and adapting foundation models for tasks like perception, reasoning, and action. This involves fine-tuning, optimization, experimentation, and building evaluation frameworks, with a contribution to data and training workflows. The goal is to enable generalization, multi-modal learning, and skill acquisition in robots operating at Amazon's scale.

This role focuses on developing advanced machine learning approaches and agentic systems for trust and safety in AWS cloud communication services. The primary goal is to create behavioral detection models and intelligent resource allocation algorithms that adapt to evolving threats and optimize service delivery. The role involves researching novel AI agent applications in security, integrating science components into production, and conducting rigorous experimentation.

Senior Applied Scientist role focused on building state-of-the-art ML models for healthcare challenges within Amazon's Special Projects team. The role emphasizes practical implementation, driving ML advancements, and delivering products to market in an entrepreneurial, startup-like environment. Requires a strong background in AI/ML, leadership skills, and the ability to translate research into actionable plans and practical solutions.

Director of Software Development for Alexa Connections, leading a team to build communication experiences and AI primitives using generative AI, LLMs, voice, and GUI. Focuses on customer-facing UX, communication systems, third-party interfaces, and LLM architecture optimization for Alexa+.

Data Scientist to build and launch production-grade agentic capabilities for Amazon Seller Assistant, a multi-agent GenAI system. Responsibilities include analyzing seller pain points, designing measurement frameworks, applying NLP and statistical modeling, and collaborating with cross-functional teams to improve the seller experience at Amazon's scale.

Applied Scientist II role focused on building AI-based services for Amazon Q Developer, aiming to redefine developer workflows. The role involves working on ambiguous problem areas, driving the delivery of end-to-end modeling solutions, and collaborating with other AWS AI services. The team builds AI products deployed in IDEs, AWS console, and web tools, providing developers with AI assistants for code generation and AWS interaction.

Software Engineer role focused on enhancing collective algorithms and topologies for optimal AI training performance on Amazon's Trainium chips. This involves optimizing communication primitives to scale AI compute across data centers, working closely with hardware teams, and developing C/C++ implementations for training LLMs.

Principal Data Scientist role at Amazon ProServe, focusing on architecting and implementing enterprise-scale AI/ML and generative AI solutions for customers. Requires technical leadership, strategic advisory, and developing reusable frameworks. Involves customer-facing engagements and mentoring junior data scientists. Requires Top Secret clearance.

Principal Data Scientist role at Amazon ProServe, focusing on architecting and implementing enterprise-scale AI/ML and generative AI solutions for AWS customers. Requires technical leadership, strategic advisory, and driving customer adoption of AWS AI/ML services. Involves leading complex initiatives, translating business challenges into technical solutions, and developing reusable frameworks. Requires a Top Secret security clearance.

Develop computer vision models on overhead imagery for a government customer, owning the entire ML development lifecycle from data exploration and feature engineering to model training, evaluation, and delivery. This role operates on classified networks and requires a Top Secret security clearance.

Senior Machine Learning Engineer role focused on designing, implementing, and scaling AI/ML solutions for AWS customers. The role involves working with customers to understand their needs, select and fine-tune models, develop proof-of-concepts, and implement AI/ML solutions at scale. It also includes designing and running experiments, researching new algorithms, and optimizing for business impact. The role requires expertise in machine learning, generative AI, and best practices, with a focus on customer success and AI transformation.

Applied Scientist role at Amazon Advertising focused on building LLM-based solutions for advertiser support, predicting problems, and coaching users. The role involves applying NLP techniques, developing scalable ML solutions, and working with AWS services to create customer-facing applications.

Principal Applied Scientist at AWS Marketplace & Partner Services focused on developing and evaluating next-generation search, recommendation, and agentic systems to drive AWS revenue growth. The role involves defining technical strategy, leading innovations in information retrieval, recommendation systems, LLMs, and agentic AI, and mentoring other scientists. Key responsibilities include architecting agentic AI systems, bridging theory with practice, and contributing to the scientific community.

Senior Applied Scientist role focused on building machine learning and GenAI solutions, specifically agentic frameworks, to improve customer support for Amazon's selling partners. The role involves end-to-end development, collaboration with engineers and product owners, and applying state-of-the-art ML/GenAI techniques to automate workflows and diagnose issues.

This role focuses on building and optimizing the systems and silicon that power AI infrastructure, including custom ML accelerator chips, distributed training systems, and compiler optimizations for ML training. It's an early career role within Annapurna Labs at AWS, aiming to accelerate AI development.

Senior Software Engineer to lead technical vision and innovation for a new team building a horizontal agentic AI layer for Amazon Advertising. The role involves architecting and implementing robust systems using LLMs and autonomous agents to transform advertiser interactions with the platform.

Senior leader to build and lead a team of ProServe Cloud Architects specializing in AI, GenAI, and Agentic AI within the Healthcare and Life Sciences domain. The role involves counseling executives on AI transformation programs, developing repeatable partnership models, and designing scalable AI solutions. Requires expertise in AI/GenAI/Agentic AI within HCLS and experience in business development or professional services.

ML Engineer role focused on developing customization capabilities like fine-tuning and distillation for LLMs, advancing LLM training techniques, and optimizing multimodal LLMs and Generative AI solutions. Requires experience deploying LLMs in production and knowledge of ML frameworks.

Machine Learning Engineer II on the AGI Customization team at Amazon, focusing on developing and optimizing LLM training techniques, including fine-tuning, distillation, model evaluation, and prompt optimization for multimodal LLMs and Generative AI solutions.

Applied Scientist role focused on developing and implementing novel foundation models for logistics, involving multimodal data, training at scale, and inference. The role spans from data preparation to model training, evaluation, and inference, with a focus on production environments.

Senior Software Development Engineer role focused on leading the development and implementation of a generative marketing agentic framework (GeMA) at Amazon. The role involves designing a multi-agent architecture, establishing evaluation frameworks, and integrating AI-based solutions for personalized marketing at scale. It requires technical leadership, collaboration with cross-functional teams, and research into LLMs and multi-agent AI systems.

This role focuses on developing advanced robotics systems that integrate AI, control systems, and mechanical design for automation. The scientist will lead the design and implementation of methods for Visual SLAM, navigation, and spatial reasoning, leveraging simulation and real-world data for model development. The goal is to create a hierarchical system combining low-level control with high-level planning for dexterous manipulation and human-robot interaction.

Software Development Engineer III at Amazon Annapurna Labs, focused on building AI agents and tools to simplify and accelerate customer adoption of AWS Neuron, the software stack for Amazon's ML silicon (Trainium). The role involves technical leadership, research, and delivery of innovative software solutions to improve ML workload porting and optimization on AWS hardware.

Applied Scientist II role focused on designing, developing, and deploying Generative AI and LLM solutions for Amazon. The role involves working with foundation models, prompt engineering, RAG, fine-tuning, and production deployment of AI systems, with a focus on applied research and evaluation.

This role is for a Senior Software Development Manager leading a team to build AI agents and tools that simplify and accelerate customer adoption of AWS Neuron, a software stack for Amazon's Machine Learning silicon (Trainium). The focus is on applying Generative AI to improve the process of porting and optimizing ML workloads on Neuron, involving collaboration with scientists, engineers, and customers.

This role focuses on developing training environments, tasks, and integrations for scaling RL environments and core model capabilities for browser-based agents. The primary responsibility is to architect and deliver robust software solutions, including agentic harnesses, and engineer high-performance systems using TypeScript and Python.

Senior Machine Learning Engineer at AWS Applied AI Solutions focused on building a new agentic product. The role involves transforming research into production systems, owning end-to-end deployment of Generative AI and ML methods, and establishing scalable processes for model development, validation, and serving. Requires expertise in agentic systems, production ML, and scalable deployment architectures, bridging research and customer-facing products.

Software Development Manager for Amazon's Devices & Services Trust CX Innovations team, focusing on building and scaling teams that deliver privacy-first, accessible, and trustworthy AI experiences for consumer devices like Alexa and Echo. The role involves driving technical strategy for privacy-preserving AI architectures, responsible AI frameworks, and accessibility features, while balancing performance with privacy, building explainable AI systems, and creating guardrails for LLMs. Key challenges include latency vs. privacy trade-offs, AI safety at scale, ambient computing privacy, multimodal AI systems, and real-time evaluation.

Senior Applied Scientist at Amazon Ads focusing on applying cutting-edge generative AI and LLMs to the advertising life cycle. The role involves researching, developing, and deploying ML solutions for ranking, personalization, NLP, computer vision, recommender systems, and LLMs. It requires driving end-to-end projects, building and optimizing models, running A/B experiments, and developing scalable ML processes. The role emphasizes impacting millions of customers and advertisers through innovative ML solutions at massive scale.

Software Development Engineer focused on optimizing and enabling deep learning and GenAI workloads, specifically LLMs, on AWS's custom ML accelerators (Neuron SDK, Inferentia, Trainium). The role involves system-level and low-level optimizations for inference performance, working across frameworks, kernels, and hardware boundaries.

Senior Software Development Engineer for AWS Neuron, focusing on accelerating deep learning and GenAI workloads on custom ML accelerators (Inferentia and Trainium). The role involves optimizing inference performance for LLMs, working across the stack from frameworks to hardware-software boundaries, and collaborating with compiler, runtime, and hardware teams. Key responsibilities include designing, developing, and optimizing ML models and frameworks, building infrastructure for model onboarding, implementing low-level optimizations, and working with customers on model enablement.

Applied Scientist role focused on enhancing AWS software stack for Trainium and Inferentia accelerators, involving ML/RL for kernel/code generation, ML compiler techniques, system robustness, and efficient kernel development. Collaborates with customers and engineering teams to optimize ML systems and adoption.

Principal Applied Scientist role focused on developing and deploying generative AI solutions for Amazon's Sponsored Products and Brands advertising platform. The role involves defining science vision, building ML/LLM models for advertiser and shopper experiences, optimizing campaign performance, and leading scientific rigor. Requires strong ML, LLM, and GenAI expertise with experience in production systems and digital advertising.

Software Development Engineer focused on optimizing and enabling AI/ML model inference on AWS's custom hardware accelerators (Inferentia and Trainium), working across the stack from frameworks like PyTorch/JAX to hardware-specific optimizations and kernel development.

Senior Manager role leading a team of Applied Scientists and Engineers to develop and deploy generative AI solutions for Amazon's Sponsored Products and Brands advertising platform, focusing on multi-lingual and multi-modal applications to drive growth in non-US markets.

Machine Learning Systems Engineer for Frontier AI Robotics team, focusing on building and optimizing distributed training infrastructure for large-scale deep learning and transformer models. Role involves engineering scalable, high-performance systems for AI research and applications, with a focus on robotics, multimodal perception, and manipulation strategies. Requires strong software development, ML infrastructure, and deep learning framework expertise.

Lead a team to invent and build the SPB-Agent, a GenAI platform transforming retail-media advertising for Amazon advertisers. This agent will act as an intelligent advisor integrated into Amazon Ad Console and Seller/Vendor portals, using conversational interfaces and deep reasoning to help advertisers discover growth opportunities, optimize campaigns, and execute strategies at scale.