Computer programmer Jobs in Seattle, WA

Do you want to create the greatest-possible worldwide impact in Robotics? Amazon has the world's most exciting treasure trove of robotics challenges. At Amazon Robotics we build high-performance, real-time robotic systems that can perceive, learn, and act intelligently alongside humans—at Amazon scale. Amazon Robotics invents and scales AI systems for robotics in fulfillment. Our mission is to enable robots to interact safely, efficiently, and fluently high density real-world fulfillment centers. Our AI solutions enable robots to learn from their own experiences, from each other, and from humans to build intelligence that feeds itself.

We hire and develop subject matter experts in AI with a focus on 3D perception, computer vision, deep learning, and generative modeling. We target high-impact algorithmic unlocks in areas such as 3D scene understanding and completion, semantic occupancy prediction, multi-view 3D reconstruction, depth estimation, shape completion, and real-time inference - all of which have high-value impact for our current and future fulfillment networks.

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.


Key job responsibilities
- 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.

A day in the life
Amazon offers a full range of benefits for you and eligible family members, including domestic partners. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include:
1. Medical, Dental, and Vision Coverage
2. Maternity and Parental Leave Options
3. Paid Time Off (PTO)
4. 401(k) Plan

If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!

About the team


The Vulcan Stow Perception team builds the visual intelligence that enables Amazon's next-generation robotic stow systems to understand and interact with densely packed fulfillment environments. We own the full perception stack—from raw sensor input to actionable 3D scene representations—powering robots that autonomously stow millions of items daily across Amazon's global network.

Our team tackles some of the hardest unsolved problems in 3D robotic perception: completing occluded scenes from partial observations, generating real-time semantic occupancy predictions, fusing multi-camera inputs (pedestal and end-of-arm tool), and producing sub-250ms mesh reconstructions that drive downstream manipulation decisions. We operate at the intersection of research and production-scale deployment, building systems that must be both scientifically rigorous and operationally bulletproof.

We are a tight-knit group of applied scientists and engineers who ship models that run on real robots in real fulfillment centers—not just papers or prototypes. Our culture values technical depth, rapid experimentation, and end-to-end ownership. If you want to push the boundaries of 3D computer vision, work with transformer and generative architectures at the frontier, and see your work directly impact how millions of packages reach customers - this is the team.

BASIC QUALIFICATIONS

- 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.

PREFERRED QUALIFICATIONS

- 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.

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