Amazon Science

From warehouse automation to surgical assistance, many real-world applications depend on robots performing delicate, contact-intensive tasks. Often missing in these situations is the sense of touch: robots need to feel the forces on their fingertips to manipulate objects effectively. Despite years of effort, robust and scalable solutions to this problem remain out of reach, especially in industrial settings.

We are looking for an Applied Scientist to join the Robotics Simulation team at Amazon Robotics. In this role you will design, build, and validate the simulation environments and policy training pipelines that enable robots to learn manipulation and mobility skills in simulation and transfer them to real hardware.

Reinforcement learning (RL) is one of the techniques we use to make language models better at sustained, multistep tasks like writing code, navigating a website, or carrying out a research workflow. The model doesn't act alone in those settings; it's wrapped in a piece of software we call a harness, which lets it call tools, observe the results of using them, and decide what to do next.

Test-time scaling via sequential revision has emerged as a powerful paradigm for enhancing Large Language Model (LLM) reasoning. However, standard post-training methods primarily optimize single-shot objectives, creating a fundamental misalignment with multi-step inference dynamics.

Protein design requires extrapolating beyond training data to achieve higher fitness. State-of-the-art methods typically fine-tune billion-parameter language models end-to-end, often combined with external scorers, data distillation, and multiple rounds of iterative refinement.

Tabular foundation models like TabPFN and TabICL achieve state-of-the-art performance through in-context learning, yet their architectures remain fundamentally opaque. We introduce KernelICL, a framework to enhance tabular foundation models with quantifiable sample-based inspectability.

Expo Talk: Strands robots: Unifying robot control, simulation, and training behind natural language July 7, 12:00 AM - 1:00 AM KST Room: Hall B2 Speakers: Cagatay Cali, Yin Song Abstract: Despite rapid advances in vision-language-action (VLA) models, deploying robot intelligence remains fragmented: different SDKs for different robots, different policy frameworks with incompatible interfaces, an unbridged simulation-to-reality gap, and training pipelines that demand specialist expertise .

At Amazon, we believe that as our strategy to reach net-zero by 2040 evolves, we need to continue to raise the bar on what and how we measure. Measuring carbon emissions across our entire business is complex, and the tools and methodologies available are continuously improving.

Multi-Agent Debate (MAD) frameworks improve factual reliability in large language models (LLMs) by allowing agents to critique and refine one another's reasoning. Yet, existing MAD systems are computationally expensive and prone to degradation under prolonged debates due to redundant exchanges and unstable judging.

In this paper we introduce SemiGPC, a distribution-aware label refinement strategy based on Gaussian Processes where the predictions of the model are derived from the labels posterior distribution. Differently from other buffer-based semi-supervised methods such as Co-Match [17] and SimMatch [34], our SemiGPC includes a normalization term that addresses imbalances in the global data distribution while maintaining local sensitivity.