Software Engineer role focused on optimizing collective operations for AWS Trainium, a purpose-built AI training chip. The role involves enhancing collective algorithms and topologies, optimizing compute for specific LLM training topologies, and working closely with hardware teams to maximize performance using C/C++. The goal is to scale AI compute across the data center for training frontier AI models.

Principal Technical Product Manager to define and drive product strategy for training software on AWS Trainium, including distributed training libraries, post-training workflows (RLHF, DPO, fine-tuning), reinforcement learning frameworks, and training performance optimization. The role focuses on enabling researchers and operators to train frontier models at scale.

Develop training infrastructure for large-scale reinforcement learning on LLMs, working across the technology stack including ML systems, orchestration, and data management. Analyze, troubleshoot, and profile ML systems, and conduct MLSys research for new techniques and tooling.

Senior Software Engineer role focused on the distributed training of large-scale ML models (LLMs, Stable Diffusion, ViTs) on AWS custom silicon (Trainium, Inferentia). Responsibilities include leading efforts to build distributed training support in PyTorch and JAX, optimizing models for performance and efficiency, and working with chip architects and compiler engineers. Requires strong software development skills, ML foundation, and experience with distributed training libraries.

Senior Applied Scientist role focused on developing 3D physics-based simulation environments and tools for robotics, specifically for training large-scale machine learning models using reinforcement learning and synthetic data generation. The role involves establishing processes, building real-to-sim workflows, and minimizing sim-to-real gaps, with a secondary focus on enabling agentic systems through simulation.

The role focuses on developing and applying cutting-edge simulation methodologies for advanced robotics systems, including physics-based simulation, sim-to-real transfer, and machine learning. The goal is to enable rapid development, testing, and validation of robotic systems in complex environments. The role involves fundamental research and real-world development, translating research into scalable simulation capabilities that impact robot design and building.

Software Development Engineer role focused on building the platform for validating safety-critical autonomous systems. This involves designing scenario generation pipelines, integrating generative AI models for realistic behaviors, creating synthetic sensor data, and developing export connectors for simulation platforms. The role spans the full lifecycle from data curation to deployment monitoring, with a focus on automating testing and exploring edge cases.

This role focuses on advancing knowledge graphs for the LLM era, specifically for LLM grounding and construction pipelines. It involves web-scale knowledge mining, fact verification, multilingual information retrieval, and agent memory over graphs. The primary responsibility is entity resolution for conflating facts from multiple sources into a single graph entity, requiring scalable, generic, and streaming data solutions. The role also touches upon agent memory, suggesting a secondary stage involvement.

This internship focuses on developing systems and frameworks for machine learning asset lifecycle management, leveraging NLP and information retrieval. The role involves research into ML operations and knowledge engineering to enhance Amazon's ML capabilities.

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.

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.

Senior Software Development Engineer focused on ML Data Infrastructure for Frontier AI Robotics at Amazon. The role involves building and maintaining scalable data infrastructure, designing dataset management systems, developing visualization tools, and implementing advanced data filtering techniques to support cutting-edge AI robotics research. Collaboration with science teams is key, requiring both infrastructure development and hands-on technical contribution to data preparation.

Seeking a Simulation Engineer to join an AI robotics research team, focusing on developing 3D physics-based simulation frameworks and tools to enable large-scale machine learning model training for robotics. The role involves developing simulations for reinforcement learning, closed-loop simulations, synthetic data generation, implementing robotics features, building real-to-sim workflows, and collaborating with ML researchers.

Senior Software Development Engineer focused on acquiring, creating, and curating high-quality software engineering datasets for training Generative AI models, including Reinforcement Learning. The role involves designing and building automated systems for data mining and quality assessment, as well as developing GenAI-powered workflow tools to streamline data collection and assurance processes.

Senior Software Engineer on the Velocity team at Amazon, focusing on perception and localization for autonomous robots. The role involves developing ML capabilities and infrastructure for perception/localization, optimizing algorithm performance, and building frameworks for data replay, analysis, and resource management on embedded systems.

This role is for a PhD student intern focused on research science at Amazon. The intern will apply advanced statistical techniques and AI/ML technologies to solve complex problems, implement prototypes, and work with large datasets. Responsibilities include data collection, analysis, interpretation, developing and testing hypotheses, documenting findings, and collaborating with team members. The role emphasizes hands-on scientific work, data analysis, experimental design, and computational modeling.

Data Scientist II role focused on developing and supporting data science methodologies and models for Amazon's global inventory management and buying programs. The role involves deriving causal inferences, modeling variations, and building tools to drive key decisions in buying and sourcing strategies, with a focus on massive datasets and operational efficiency.

Seeking a Senior Research Scientist to lead the design and implementation of autonomous experimental research platforms leveraging data science and AI for sustainable materials innovation. This role combines Physical AI with materials chemistry to accelerate discovery and validation, establishing scientific strategy and technical roadmaps for next-generation autonomous capabilities.

The role focuses on building the AI/ML foundation for sustainability initiatives, involving data identification, ingestion, harmonization, storage, and maintenance of strategic models and data. It requires developing data pipelines, implementing data collection strategies, and creating monitoring and alarming systems for sustainability-related AI solutions.

This role focuses on building the AI/ML foundation for sustainability initiatives, involving data and AI/ML infrastructure implementation, data pipeline development, and establishing ML Ops best practices. The goal is to support various sustainability efforts like carbon footprinting and climate risk monitoring.

Data Scientist role focused on building predictive models and optimization algorithms for AWS Support capacity planning. The role involves forecasting contact and volume, identifying business opportunities using statistics and machine learning, and partnering with various teams to drive operational improvements. Requires experience in data science, forecasting, optimization, and machine learning, with a focus on applying theoretical models in an applied environment using Python or R.

Senior Applied Scientist role focused on designing, implementing, and scaling decision-making algorithms for AWS EC2 capacity management. The role involves mathematical optimization, large-scale problem solving, and applying ML/Gen AI methods to enhance optimization algorithms. Responsibilities include data analysis, prescriptive optimization modeling, simulation, A/B testing, and collaborating with engineering and product teams.

This role focuses on providing strategic oversight and mentorship for human-in-the-loop and model-in-the-loop data pipelines, ensuring data quality for LLM training and evaluations. The consultant will collaborate with AI teams, optimize data collection, and develop benchmarks for GenAI model performance.

Internship role for MS or PhD students in Speech, Robotics, Computer Vision, or Machine Learning/Deep Learning. Focuses on building large-scale ML systems using large datasets and translating business requirements into prototypes. Involves analyzing and modeling terabytes of text, images, and other data.

The Language Engineer will develop diverse datasets for training and evaluating Amazon's AI models, utilizing synthetic data generation, model-supported generation, and human-in-the-loop approaches. This role involves designing data collections, analyzing data, building tools for data analysis/creation, and collaborating with scientists and engineers to evaluate AI model performance. Experience with multimodal data and agentic workflows is preferred.

Language Engineer role focused on dataset construction, linguistic annotation, dialog/semantic schemas, and automatic processing of large datasets for AGI Data Services. The role involves designing data collection tasks, analyzing data for insights, building data authoring tools, and collaborating with scientists to evaluate language models.

The Language Engineer will develop diverse datasets for training and evaluating Amazon's AI models, utilizing synthetic data generation, model-supported generation, and human-in-the-loop approaches. This role involves designing data collections, analyzing data, building tools for data analysis/creation, and collaborating with scientists and engineers to evaluate AI model performance. Experience with multimodal data and agentic workflows is preferred.

The Language Engineer will develop diverse datasets for training and evaluating Amazon's AI models, utilizing synthetic data generation, model-supported generation, and human-in-the-loop approaches. This role involves designing data collections, analyzing data, building tools for data analysis/creation, and collaborating with scientists and engineers to evaluate AI model performance. Experience with multimodal data and agentic workflows is preferred.

The Language Engineer will develop diverse datasets for training and evaluating Amazon's AI models, utilizing synthetic data generation, model-supported generation, and human-in-the-loop approaches. This role involves designing data collections, analyzing data, building tools for data analysis/creation, and collaborating with scientists and engineers to evaluate AI model performance. Experience with multimodal data and agentic workflows is preferred.

Manager for a Language Engineering team focused on data production and processing for AI and LLMs. Responsibilities include team leadership, technical guidance, process optimization, and collaboration with science and tech partners.

Backend engineer responsible for building and operating core services that ingest, process, and distribute large-scale, multi-modal datasets to internal tools and data pipelines for an AGI research lab. Focuses on designing backend architecture, defining operational standards, and ensuring production health, performance, and observability.

Software Engineer on the Research Tools Platform team at Amazon AGI Autonomy, responsible for building tools that support model development, data creation, evaluation, and experimentation for AI agents. The role involves defining system architecture, building UI components, and collaborating with researchers and engineers.

This role focuses on providing domain expertise to human-in-the-loop and model-in-the-loop data pipelines for LLM training and evaluations. The Senior Domain Expert Lead will ensure data quality, optimize data collection, and mentor junior team members, while staying updated on GenAI applications in their domain.

The Language Engineer II role focuses on improving Alexa Shopping features by generating high-quality data, running experiments, and prompt engineering Large Language Models for API delivery. This involves analyzing and automating processes for collecting and annotating LLM inputs/outputs, applying Generative AI techniques, and optimizing data representation of human language. The role collaborates with scientists, engineers, and program managers to enhance the voice shopping experience and owns customer-facing ML models for specific features.

This role focuses on developing diverse datasets for training and evaluating AI models, utilizing synthetic data generation, model-based generation, and human-in-the-loop approaches. The Senior Language Engineer will define data creation strategies, lead complex data collections, and analyze large datasets. They will also build tools for data analysis and creation, and collaborate with scientists to evaluate AI model performance.

The role focuses on building and maintaining a Spark-based infrastructure for processing large multimodal datasets critical for machine learning research, specifically for state-of-the-art agents within the AGI Autonomy organization. It involves data extraction, transformation, and creating tooling for researchers, with a secondary focus on agent development.

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 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.

This role supports ML model development by performing data annotation and assisting data scientists with classification algorithms. The focus is on ensuring high-quality annotated data for training and evaluation, and improving annotation processes.

This role is for a Sr. Principal Scientist focused on Stores Economics and Science at Amazon. The scientist will work with ML scientists to validate models, translate analysis into business actions, and manage data analysis and modeling. The team uses economics, statistics, and ML to understand Amazon's economy and aims to drive AI adoption across the company.

This role focuses on developing and maintaining product knowledge ontologies and taxonomies for Amazon's e-commerce platform. The Ontologist will create semantically rich data models, ensure machine ingestion and inference capabilities, and leverage generative AI for schema creation. The goal is to optimize product discovery and customer experience by understanding customer intent and product data.

Intern role focused on foundational labeling functions for generative AI, including dialogue evaluation across text, speech, audio, image, and video data. The role involves delivering high-quality labeled data, analyzing error patterns, and proposing solutions to enhance labeling quality.

Data Platform Engineer for Fauna Robotics, focused on building foundational data systems for robotics and ML development. This role involves designing and implementing infrastructure for data collection, storage, processing, and transformation from robot sensors, video, and logs, enabling ML training and fleet performance monitoring.

This role focuses on owning the data science elements of various products, including data-based decision making, product performance optimization, and tracking. The Data Scientist III will work with product managers to drive product design and build planning, translate business problems into data requirements and metrics, and initiate the design, development, and implementation of scientific analysis projects. Responsibilities include owning analysis, modeling, system design, and development of data science solutions, as well as communicating results to the business and building consensus on data strategy.

This role focuses on leveraging econometrics and ML models to optimize advertising strategies, influence business decisions, and perform hands-on analysis with large datasets. The Economist will work closely with software engineers to productionize models and run A/B experiments, impacting millions of customers.

The Sr. Technical Program Manager will lead strategy and execution for expert data collection to support LLM training and fine-tuning. This role involves defining strategy, streamlining processes, managing cross-functional teams, and influencing engineering to build data collection solutions. The focus is on improving the quality and efficiency of data procurement for AI models.

This role focuses on the data science aspects of products, including decision making, performance optimization, and tracking. It involves working with product managers to define product design and data requirements, initiating and owning the design, development, and implementation of scientific analysis projects, and building statistical and machine learning models using large datasets. The role requires strong analytical skills, experience with scripting languages like Python, and effective communication with stakeholders.

This role manages an engineering team responsible for building external-facing software capabilities to recruit, onboard, and manage a large crowd of external annotators who provide data for training and evaluating large language models. The focus is on the platform and processes for managing this crowd, which is a critical component of the data pipeline for AI model development.