Amazon

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.

AI Platform Data Engineer responsible for designing, building, and maintaining data pipelines, curated datasets for AI/ML consumption, and AI-native self-service data platforms using an AI-first development methodology. The role emphasizes leveraging AI at every layer of the data stack, including using AI agents for code optimization, building AI-powered platforms for AI models, and deploying intelligent agents for data accessibility. Experience with Gen AI enhanced development pipelines, agentic workflows, and prompt engineering is mandatory.

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.

Software Development Manager for Payment Risk Engineering at Amazon, focusing on automating country/payment/store launches and feature engineering for ML models in fraud evaluation. The role involves driving the adoption of Generative AI to accelerate fraud prevention capabilities, automate feature creation, and reduce manual onboarding efforts. It requires leading a technical team, setting vision for GenAI transformation, and empowering the team to stay at the forefront of AI technologies.

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.

Software Development Engineer Intern Machine Learning, Amazon Robotics. The role involves simplifying ML training infrastructure, extending monitoring of ML trainings, and potentially developing perception models for warehouse robots. It requires experience with AI, Neural Networks, Tensorflow, and PyTorch, and focuses on building scalable ML solutions within a robotics context.

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.

Data Scientist II role focused on building predictive models and optimization algorithms for AWS Global Services support organization, utilizing machine learning and deep learning to drive business and operational improvements. The role involves partnering with various teams to optimize and predict across the network, with a focus on contact forecasting and cost optimization.

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.

Software Development Engineer II role focused on building and maintaining data services for training, evaluating, and benchmarking Gen-AI models. This involves Human-In-The-Loop (HIL) data labeling for Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF), as well as supporting evaluations and active learning. The role contributes to powering AWS Nova and Alexa features.

The Language Engineer will develop diverse datasets for training and evaluating Amazon AI models, using synthetic data generation, model-supported generation, and human-in-the-loop methods. This role involves designing data collections, analyzing data, building tools for data creation, and collaborating with scientists and engineers to evaluate AI model performance. Experience with speech, text, and multimodal data is required.

Software Development Engineer on the Ring AI Team, focusing on cloud services for machine learning operation pipelines that handle large-scale data and enable rapid model optimization by data scientists. The role involves working with cloud services, distributed systems, and web services to deliver a world-class platform for fast-paced AI model development.

Software Development Engineer on the Ring AI Team, focusing on cloud services for machine learning operation pipelines that handle large-scale data and enable rapid model optimization by data scientists. The role involves working with cloud services, distributed systems, and web services to deliver a world-class platform for fast-paced AI model development.

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.

The role focuses on developing diverse datasets for training and evaluating AI models, using methods like synthetic data generation, model-supported generation, and human-in-the-loop collections. The Language Engineer will design data collections, create tasks, analyze data, build tools for data analysis/creation, and collaborate on AI model performance evaluation.

The role focuses on developing diverse datasets for training and evaluating AI models, utilizing synthetic data generation, model-supported generation, and human-in-the-loop approaches. Responsibilities include designing data collections, creating data using state-of-the-art methods, analyzing data, building tools for data analysis/creation, and collaborating on AI model performance evaluation.

The Language Engineer will develop diverse datasets for training and evaluating Amazon AI models, using synthetic data generation, model-supported generation, and human-in-the-loop methods. This role involves designing data collections, analyzing data, building tools for data creation, and collaborating with scientists and engineers to evaluate AI model performance. Experience with speech, text, and multimodal data is required.

The Language Engineer will develop diverse datasets for training and evaluating Amazon AI models, using synthetic data generation, model-supported generation, and human-in-the-loop methods. This role involves designing data collections, analyzing data, building tools for data creation, and collaborating with scientists and engineers to evaluate AI model performance. Experience with speech, text, and multimodal data is required.

The role focuses on developing diverse datasets for training and evaluating AI models, utilizing synthetic data generation, model-supported generation, and human-in-the-loop approaches. Responsibilities include designing data collections, creating data with synthetic and model-based methods, analyzing data, building tools for data analysis/creation, and collaborating to evaluate AI model performance.

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.