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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. Applied Scientist - Computer Vision, Amazon Robotics - **Company:** Amazon.com, Inc. - **Location:** Seattle, WA, United States - **Experience:** Expert - **Salary:** $167,100.0 - $226,100.0 - **Contract:** Temporary contract - **Skills:** Java (Programming Language), 3d Models, Adobe InDesign, Artificial Intelligence, Computer Vision, Business Software, C++ (Programming Language), Code Review, Fault Tolerance, Python (Programming Language), Machine Learning, Robotic Automation Software, Sensor Fusion, Software Deployment, Mobile Robots, Deep Learning, ONNX (Open Neural Network Exchange) Format, TensorRT, Data Pipelines, Data Generation - **Published:** July 8, 2026 - **Apply:** https://www.jobmonkeyjobs.com/career/27831583/Sr-Applied-Scientist-Computer-Vision-Amazon-Robotics-Washington-Seattle-7375 ## About the Role We are seeking an passionate, hands-on, seasoned Senior Applied Scientist who will be deep in code and algorithms; who is technically strong in building scalable 3D perception systems across semantic scene completion, encoder-decoder and transformer architectures (e.g., VoxFormer, MonoScene), voxelized occupancy prediction, panoptic and instance segmentation, depth estimation, point cloud processing, and multi-view fusion. As a Senior Applied Scientist, you will contribute to the research and development of advanced 3D perception pipelines that enable robots to reason about occluded and partially observed environments; your work along with other top-notch scientists and engineers will deliver the world's most scalable and robust robotic perception systems. You will drive ideas to products using paradigms such as 3D generative models, query-based transformers, masked autoencoder-style completion, and scalable pseudo-ground-truth data generation., As a Senior Applied Scientist, you will also help lead and mentor our team of applied scientists and engineers. You will take on challenging perception problems - such as completing 3D bin scenes from partial observations, integrating multi-camera inputs, and optimizing inference latency for edge deployment - distill requirements, and then deliver solutions that either leverage existing academic and industrial research or utilize your own out-of-the-box but pragmatic thinking. In addition to coming up with novel solutions and prototypes, you will directly contribute to implementation while you lead. A successful candidate has excellent technical depth in 3D computer vision, scientific vision, project management skills, great communication skills, and a drive to achieve results in a collaborative team environment. You should enjoy the process of solving real-world problems that, quite frankly, haven't been solved at scale anywhere before. Along the way, we guarantee you'll get opportunities to be a disruptor, prolific innovator, and a reputed problem solver-someone who truly enables AI and robotics to significantly impact the lives of millions of consumers., 4+ years of building machine learning models for business application experience - PhD, or Master's degree and 6+ years of applied research experience - Experience programming in Java, C++, Python or related language - Experience with neural deep learning methods and machine learning - Demonstrated expertise in 3D computer vision and deep learning for robotics - spanning semantic scene completion, occupancy prediction, depth estimation, multi-view reconstruction, and real-time model deployment on edge hardware., Publications in top-tier venues (CVPR, ICCV, ECCV, NeurIPS, 3DV, CoRL) in 3D scene understanding, shape completion, or occupancy prediction. - Deep expertise in generative 3D models, vision transformers, and semantic scene completion architectures. - Experience building large-scale pseudo-ground-truth or synthetic data pipelines (100K+ samples). - Proficiency in real-time model optimization (ONNX/TensorRT) and deployment on edge hardware. - Strong foundation in 3D geometry, multi-view reconstruction, and sensor fusion. - Track record shipping ML models into production robotic systems with hard latency constraints. - Effective communicator across science, engineering, and operations stakeholders in fast-paced environments. ## Description Architect, design, and implement 3D perception models - including encoder-decoder networks, query-based transformers, and generative architectures- for semantic occupancy prediction and scene completion on robotic platforms. - Own the end-to-end model lifecycle: develop scalable training pipelines, optimize inference latency for ARM-based edge processors, and deploy production models that meet real-time performance targets. - Design and scale pseudo-ground-truth data generation pipelines - both heuristic-based and learning-based (e.g., SAM3D, shape completion) to produce curated training samples using SageMaker infrastructure. - Drive multi-view perception integration by fusing multiple view camera inputs for robust 3D reconstruction in partially observed and occluded bin environments. - Influence the team's technical strategy and contribute to the long-term vision and roadmap for 3D perception in fulfillment robotics. - Partner with cross-functional stakeholders across engineering, science, and operations teams to define requirements, iterate on system design, and deliver end-to-end solutions from research prototype to production deployment. - Maintain high standards by participating in design and code reviews, designing for fault tolerance and operational excellence, and creating mechanisms for continuous improvement. - Prototype and validate concepts through simulation, synthetic data evaluation, and live robotic workcell testing using 3D metrics (mIoU, IoU) and affordance-based evaluation frameworks. - Mentor applied scientists and engineers, raise the technical bar, and foster a culture of scientific rigor and rapid experimentation. ## Related Videos - [Robots are coming into the wild! Full-Stack Robotics Engineers, be ready!](https://www.wearedevelopers.com/videos/479-robots-are-coming-into-the-wild-full-stack-robotics-engineers-be-ready) - [TresJS a new declarative ThreeJS as Vue components](https://www.wearedevelopers.com/videos/543-tresjs-a-new-declarative-threejs-as-vue-components) - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Are Code Reviews Worth It? Insights from 16 Years of Review Data](https://www.wearedevelopers.com/videos/1135-are-code-reviews-worth-it-insights-from-16-years-of-review-data) - [How Robots Learn to be Robots](https://www.wearedevelopers.com/videos/1632-how-robots-learn-to-be-robots) - [Cross platform Augmented Reality development with React Native](https://www.wearedevelopers.com/videos/160-cross-platform-augmented-reality-development-with-react-native) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [Dev Digest 137 - AI'm not sure about this](https://www.wearedevelopers.com/magazine/485-dev-digest-137-ai-m-not-sure-about-this) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)