The breadth of Amazon's computer vision research is on display at ECCV

Research topics range from visual anomaly detection to road network extraction, regression-constrained neural-architecture search to self-supervised learning for video representations.

Amazon's contributions to this year's European Conference on Computer Vision (ECCV) reflect the diversity of the company's research interests. Below is a quick guide to the topics and methods of a dozen ECCV papers whose authors include Amazon scientists.

Fine-grained fashion representation learning by online deep clustering
Yang (Andrew) Jiao, Ning Xie, Yan Gao, Chien-Chih Wang, Yi Sun

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Fashions are characterized by both global attributes, such as “skirt length”, and local attributes, such as “neckline style”. Accurate representations of such attributes are essential to tasks like fashion retrieval and fashion recommendation, but learning representations of each attribute independently ignores shared visual statistics among the attributes. Instead, the researchers treat representation learning as a multitask learning problem, enforcing cluster-level constraints on global structure. The learned representations improve fashion retrieval by a large margin.

GLASS: Global to local attention for scene-text spotting
Roi Ronen, Shahar Tsiper, Oron Anschel, Inbal Lavi, Amir Markovitz, R. Manmatha

Modern text-spotting models combine text detection and recognition into a single end-to-end framework, in which both tasks often rely on a shared global feature map. Such models, however, struggle to recognize text across scale variations (smaller or larger text) and arbitrary word rotation angles. The researchers propose a novel attention mechanism for text spotting, called GLASS, that fuses together global and local features. The global features are extracted from the shared backbone, while the local features are computed individually on resized, high-resolution word crops with upright orientation. GLASS achieves state-of-the-art results on multiple public benchmarks, and the researchers show that it can be integrated with other text-spotting solutions, improving their performance.

GLASS.png
A novel attention mechanism for text spotting, called GLASS, fuses together global and local features. From "GLASS: Global to local attention for scene-text spotting".

Large scale real-world multi-person tracking
Bing Shuai, Alessandro Bergamo, Uta Buechler, Andrew Berneshawi, Alyssa Boden, Joseph Tighe

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This paper presents a new multi-person tracking dataset — PersonPath22 — which is more than an order of magnitude larger than existing high-quality multi-object tracking datasets. The PersonPath22 dataset is specifically sourced to provide a wide variety of conditions, and its annotations include rich metadata that allows the performance of a tracker to be evaluated along these different dimensions. Its large-scale real-world training and test data enable the community to better understand the performance of multi-person tracking systems in a range of scenarios and conditions.

MaCLR: Motion-aware contrastive Learning of representations for videos
Fanyi Xiao, Joseph Tighe, Davide Modolo

Attempts to use self-supervised learning for video have had some success, but existing approaches don’t make explicit use of motion information derived from the temporal sequence, which is important for supervised action recognition tasks. The researchers propose a self-supervised video representation-learning method that explicitly models motion cues during training. The method, MaCLR, consists of two pathways, visual and motion, connected by a novel cross-modal contrastive objective that enables the motion pathway to guide the visual pathway toward relevant motion cues.

MACLR.png
A frame of video (top left) and three different methods of capturing motion. From "MaCLR: Motion-aware contrastive Learning of representations for videos".

PSS: Progressive sample selection for open-world visual representation learning
Tianyue Cao, Yongxin Wang, Yifan Xing, Tianjun Xiao, Tong He, Zheng Zhang, Hao Zhou, Joseph Tighe

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New end-to-end approach to zero-shot video classification dramatically outperforms predecessors.

In computer vision, open-world representation learning is the challenge of learning representations for categories of images not seen during training. Existing approaches make unrealistic assumptions, such as foreknowledge of the number of categories the unseen images fall into, or the ability to determine in advance which unlabeled training examples fall into unseen categories. The researchers’ novel progressive approach avoids such assumptions, selecting at each iteration unlabeled samples that are highly homogenous but belong to classes that are distant from the current set of known classes. High-quality pseudo-labels generated via clustering over these selected samples then improve the feature generalization iteratively.

Rayleigh EigenDirections (REDs): Nonlinear GAN latent space traversals for multidimensional features
Guha Balakrishnan, Raghudeep Gadde, Aleix Martinez, Pietro Perona

Generative adversarial networks (GANs) can map points in a latent space to images, producing extremely realistic synthetic data. Past attempts to control GANs’ outputs have looked for linear trajectories through the space that correspond, approximately, to continuous variation of a particular image feature. The researchers propose a new method for finding nonlinear trajectories through the space, providing unprecedented control over GANs’ outputs, including the ability to hold specified image features fixed while varying others.

Rethinking few-shot object detection on a multi-domain benchmark
Kibok Lee, Hao Yang, Satyaki Chakraborty, Zhaowei Cai, Gurumurthy Swaminathan, Avinash Ravichandran, Onkar Dabeer

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New “meta-learning” approach improves on the state of the art in “one-shot” learning.

Most existing work on few-shot object detection (FSOD) focuses on settings where both the pretraining and few-shot learning datasets are from similar domains. The researchers propose a Multi-dOmain Few-Shot Object Detection (MoFSOD) benchmark consisting of 10 datasets from a wide range of domains to evaluate FSOD algorithms across a greater variety of applications. They comprehensively analyze the effects of freezing layers, different architectures, and different pretraining datasets on FSOD performance, drawing several surprising conclusions. One of these is that, contrary to prior belief, on a multidomain benchmark, fine-tuning (FT) is a strong baseline for FSOD.

SPot-the-Difference: Self-supervised pre-training for anomaly detection and segmentation
Yang Zou, Jongheon Jeong, Latha Pemula, Dongqing Zhang, Onkar Dabeer

Visual anomaly detection is commonly used in industrial quality inspection. This paper presents a new dataset and a new self-supervised learning method for ImageNet pretraining to improve anomaly detection and segmentation in 1-class and 2-class 5/10/high-shot training setups. The Visual Anomaly (VisA) Dataset consists of 10,821 high-resolution color images (9,621 normal and 1,200 anomalous samples) covering 12 objects in three domains, making it one of the largest industrial anomaly detection datasets to date. The paper also proposes a new self-supervised framework — SPot-the-Difference (SPD) — that can regularize contrastive self-supervised and also supervised pretraining to better handle anomaly detection tasks.

SPD contrastive learning.png
Conventional contrastive learning (left) and the contrastive-learning scheme used in SPD (spot-the-difference) training. From "SPot-the-difference: Self-supervised pre-training for anomaly detection and segmentation".

TD-Road: Top-down road network extraction with holistic graph construction
Yang He, Ravi Garg, Amber Roy Chowdhury

Road network extraction from satellite imagery is essential for constructing rich maps and enabling numerous applications in route planning and navigation. Previous graph-based methods used a bottom-up approach, estimating local information and extending a graph iteratively. This paper, by contrast, proposes a top-down approach that decomposes the problem into two subtasks: key point prediction and connectedness prediction. Unlike previous approaches, the proposed method applies graph structures (i.e., locations of nodes and connections between them) as training supervisions for deep neural networks and directly generates road graph outputs through inference.

TD-road.png
A satellite image (left) and three methods for extracting road networks from it: segmentation, bottom-up-graph-based methods, and a new top-down graph-based method (far right). From "TD-Road: Top-down road network extraction with holistic graph construction."

Towards regression-free neural networks for diverse compute platforms
Rahul Duggal, Hao Zhou, Shuo Yang, Jun Fang, Yuanjun Xiong, Wei Xia

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New approach corrects for cases when average improvements are accompanied by specific regressions.

Commercial machine learning models are constantly being updated, and while an updated model may improve performance on average, it can still regress — i.e., suffer “negative flips” — on particular inputs it used to handle correctly. This paper introduces regression-constrained neural-architecture search (REG-NAS), which consists of two components: (1) a novel architecture constraint that enables a larger model to contain all the weights of a smaller one, thus maximizing weight sharing, and (2) a novel search reward that incorporates both top-1 accuracy and negative flips in the architecture search metric. Relative to the existing state-of-the-art approach, REG-NAS enables 33 – 48% reduction of negative flips.

Unsupervised and semi-supervised bias benchmarking in face recognition
Alexandra Chouldechova, Siqi Deng, Yongxin Wang, Wei Xia, Pietro Perona

This paper introduces semi-supervised performance evaluation for face recognition (SPE-FR), a statistical method for evaluating the performance and algorithmic bias of face verification systems when identity labels are unavailable or incomplete. The method is based on parametric Bayesian modeling of face embedding similarity scores, and it produces point estimates, performance curves, and confidence bands that reflect uncertainty in the estimation procedure. Experiments show that SPE-FR can accurately assess performance on data with no identity labels and confidently reveal demographic biases in system performance.

X-DETR: A versatile architecture for instance-wise vision-language tasks
Zhaowei Cai, Gukyeong Kwon, Avinash Ravichandran, Erhan Bas, Zhuowen Tu, Rahul Bhotika, Stefano Soatto

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This paper addresses the challenge of instance-wise vision-language tasks, which require free-form language to align with objects inside an image, rather than the image itself. The paper presents the X-DETR model, whose architecture has three major components: an object detector, a language encoder, and a vision-language alignment module. The vision and language streams are independent until the end, and they are aligned using an efficient dot-product operation. This simple architecture shows good accuracy and fast speeds for multiple instance-wise vision-language tasks, such as open-vocabulary object detection.

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X-DETR addresses the challenge of instance-wise vision-language tasks, which require free-form language to align with objects inside an image, rather than the image itself. From "X-DETR: A versatile architecture for instance-wise vision-language tasks".

Research areas

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US, MA, North Reading
We are looking for experienced scientists and engineers to explore new ideas, invent new approaches, and develop new solutions in the areas of Controls, Dynamic modeling and System identification. Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers that work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun. Key job responsibilities Applied Scientists take on big unanswered questions and guide development team to state-of-the-art solutions. We want to hear from you if you have deep industry experience in the Mechatronics domain and : * the ability to think big and conceive of new ideas and novel solutions; * the insight to correctly identify those worth exploring; * the hands-on skills to quickly develop proofs-of-concept; * the rigor to conduct careful experimental evaluations; * the discipline to fast-fail when data refutes theory; * and the fortitude to continue exploring until your solution is found We are open to hiring candidates to work out of one of the following locations: North Reading, MA, USA | Westborough, MA, USA
GB, London
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Python or R is necessary, and experience with SQL and UNIX would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. We are open to hiring candidates to work out of one of the following locations: London, GBR
DE, BE, Berlin
Are you excited about developing state-of-the-art computer vision models that revolutionize Amazon’s Fulfillment network? Are you looking for opportunities to apply AI on real-world problems at truly vast scale? At Amazon Fulfillment Technologies and Robotics, we are on a mission to build high-performance autonomous systems that perceive and act to further improve our world-class customer experience — at Amazon scale. To this end, we are looking for an Applied Scientist who will build and deploy models that make smarter decisions on a wide array of multi-modal signals. Together, we will be pushing beyond the state of the art in optimizing one of the most complex systems in the world: Amazon's Fulfillment Network. Key job responsibilities In this role, you will build computer vision and multi-modal deep learning models that understand the state of products and packages flowing through Amazon’s fulfillment network. You will build models that solve challenging problems like product identification and damage detection on Amazon's entire retail catalog (billions of different items, thousands of new items every day). You will primarily work with very large real-world vision datasets, as well as a diverse set of multi-modal datasets, including natural language and structured data. You will face a high level of research ambiguity and problems that require creative, ambitious, and inventive solutions. A day in the life AFT AI delivers the AI solutions that empower Amazon’s fulfillment network to make smarter decisions. You will work on an interdisciplinary team of scientists and engineers with deep expertise in developing cutting-edge AI solutions at scale. You will work with images, videos, natural language, and sequences of events from existing or new hardware. You will adapt state-of-the-art machine learning and computer vision techniques to develop solutions for business problems in the Amazon Fulfillment Network. About the team Amazon Fulfillment Technologies (AFT) powers Amazon’s global fulfillment network. We invent and deliver software, hardware, and science solutions that orchestrate processes, robots, machines, and people. We harmonize the physical and virtual world so Amazon customers can get what they want, when they want it. AFT AI is spread across multiple locations in NA (Bellevue WA and Nashville, TN) and Europe (Berlin, Germany). We are hiring candidates to work out of the Berlin location. Publicly available articles showcasing some of our work: - Damage Detection: https://www.amazon.science/latest-news/the-surprisingly-subtle-challenge-of-automating-damage-detection - Product ID: https://www.amazon.science/latest-news/how-amazon-robotics-is-working-on-new-ways-to-eliminate-the-need-for-barcodes We are open to hiring candidates to work out of one of the following locations: Berlin, BE, DEU
LU, Luxembourg
Have you ever wished to build high standard Operations Research and Machine Learning algorithms to optimize one of the most complex logistics network? Have you ever ordered a product on Amazon websites and wondered how it got delivered to you so fast, and what kinds of algorithms & processes are running behind the scenes to power the whole operation? If so, this role is for you. The team: Global transportation services, Research and applied science - Operations is at the heart of the Amazon customer experience. Each action we undertake is on behalf of our customers, as surpassing their expectations is our passion. We improve customer experience through continuously optimizing the complex movements of goods from vendors to customers throughout Europe. - Global transportation analytical teams are transversal centers of expertise, composed of engineers, analysts, scientists, technical program managers and developers. We are focused on Amazon most complex problems, processes and decisions. We work with fulfillment centers, transportation, software developers, finance and retail teams across the world, to improve our logistic infrastructure and algorithms. - GTS RAS is one of those Global transportation scientific team. We are obsessed by delivering state of the art OR and ML tools to support the rethinking of our advanced end-to-end supply chain. Our overall mission is simple: we want to implement the best logistics network, so Amazon can be the place where our customers can be delivered the next-day. The role: Applied scientist, speed and long term network design The person in this role will have end-to-end ownership on augmenting RAS Operation Research and Machine Learning modeling tools. They will help understand where are the constraints in our transportation network, and how we can remove them to make faster deliveries at a lower cost. You will be responsible for designing and implementing state-of-the-art algorithmic in transportation planning and network design, to expand the scope of our Operations Research and Machine Learning tools, to reflect the constantly evolving constraints in our network. You will enable the creation of a product that drives ever-greater automation, scalability and optimization of every aspect of transportation, planning the best network and modeling the constraints that prevent us from offering more speed to our customer, to maximize the utilization of the associated resources. The impact of your work will be in the Amazon EU global network. The product you will build will span across multiple organizations that play a role in Amazon’s operations and transportation and the shopping experience we deliver to customer. Those stakeholders include fulfilment operations and transportation teams; scientists and developers, and product managers. You will understand those teams constraints, to include them in your product; you will discuss with technical teams across the organization to understand the existing tools and assess the opportunity to integrate them in your product.You will engage with fellow scientists across the globe, to discuss the solutions they have implemented and share your peculiar expertise with them. This is a critical role and will require an aptitude for independent initiative and the ability to drive innovation in transportation planning and network design. Successful candidates should be able to design and implement high quality algorithm solutions, using state-of-the art Operations Research and Machine Learning techniques. Key job responsibilities - Engage with stakeholders to understand what prevents them to build a better transportation network for Amazon - Review literature to identify similar problems, or new solving techniques - Build the mathematical model representing your problem - Implement light version of the model, to gather early feed-back from your stakeholders and fellow scientists - Implement the final product, leveraging the highest development standards - Share your work in internal and external conferences - Train on the newest techniques available in your field, to ensure the team stays at the highest bar About the team GTS Research and Applied Science is a team of scientists and engineers whom mission is to build the best decision support tools for strategic decisions. We model and optimize Amazon end-to-end operations. The team is composed of enthusiastic members, that love to discuss any scientific problem, foster new ideas and think out of the box. We are eager to support each others and share our unique knowledge to our colleagues. We are open to hiring candidates to work out of one of the following locations: Luxembourg, LUX
US, CA, Santa Clara
Amazon AI is looking for world class scientists and engineers to join its AWS AI Labs. This group is entrusted with developing core data mining, natural language processing, deep learning, and machine learning algorithms for AWS. You will invent, implement, and deploy state of the art machine learning algorithms and systems. You will build prototypes and explore conceptually new solutions. You will interact closely with our customers and with the academic community. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists. Inclusive Team Culture Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Work/Life Balance Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives. Mentorship & Career Growth Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future. We are open to hiring candidates to work out of one of the following locations: New York, NY, USA | Santa Clara, CA, USA | Seattle, WA, USA
IN, KA, Bengaluru
Job Description ATE (Analytics, Technology and Engineering) is a multi-disciplinary team of scientists, engineers, and technicians, all working to innovate in operations for the benefit of our customers. Our team is responsible for creating core analytics, science capabilities, platforms development and data engineering. We develop scalable analytics applications and research modeling to optimize operation processes.. You will work with professional software development managers, data engineers, data scientists, applied scientists, business intelligence engineers and product managers using rigorous quantitative approaches to ensure high quality data tech products for our customers around the world, including India, Australia, Brazil, Mexico, Singapore and Middle East. We are on the lookout for an enthusiastic and highly analytical individual to be a part of our journey. Amazon is growing rapidly and because we are driven by faster delivery to customers, a more efficient supply chain network, and lower cost of operations, our main focus is in the development of strategic models and automation tools fed by our massive amounts of available data. You will be responsible for building these models/tools that improve the economics of Amazon’s worldwide fulfillment networks in emerging countries as Amazon increases the speed and decreases the cost to deliver products to customers. You will identify and evaluate opportunities to reduce variable costs by improving fulfillment center processes, transportation operations and scheduling, and the execution to operational plans. You will also improve the efficiency of capital investment by helping the fulfillment centers to improve storage utilization and the effective use of automation. Finally, you will help create the metrics to quantify improvements to the fulfillment costs (e.g., transportation and labor costs) resulting from the application of these optimization models and tools. Major responsibilities include: · In this role, you will be responsible for developing and implementing innovative, scalable models and tools aimed at tackling novel challenges within Amazon’s global fulfillment network. Collaborating with fellow scientists from various teams, you will work on integrated solutions to enhance fulfillment speed, reduce costs. Your in-depth comprehension of business challenges will enable you to provide scientific analyses that underpin critical business decisions, utilizing a diverse range of methodologies. You’ll have the opportunity to design scientific tool platforms, deploy models, create efficient data pipelines, and streamline existing processes. Join us in shaping the future of Amazon’s global retail business by optimizing delivery speed at scale and making a lasting impact on the world of e-commerce. If you’re passionate about solving complex problems and driving innovation, we encourage you to apply. About the team This team is responsible for applying science based algo and techniques to solve the problems in operation and supply chain. Some of these problems include, volume forecasting, capacity planning, fraud detection, scenario simulation and using LLM/GenAI for process efficiency We are open to hiring candidates to work out of one of the following locations: Bengaluru, KA, IND
IL, Tel Aviv
Are you passionate about pushing the boundaries of computer vision, generative AI, deep learning, and machine learning? Ready to tackle challenges in document understanding at scale? We’re looking for innovative minds to join our world-class team at AWS, where you’ll collaborate with leading researchers, academics, and engineers on Amazon Textract. Why AWS? Be part of the leading cloud service provider powering innovation and positive impact. Work on real-world problems alongside tech and business giants. Access to unlimited data and computational resources. Collaborate with world-class researchers and developers. Deploy solutions at AWS scale and publish your work at top conferences. Focus Areas: - LLMs, document understanding, scene text recognition. - Visual question answering, NLP+vision, layout understanding. Locations: Tel Aviv and Haifa Think you’re a fit? Dive into the world of AWS Computer Vision and help us innovate at the forefront of technology. Key job responsibilities - Design cutting-edge neural network architectures. - Create document understanding solutions for complex scenarios and large visual datasets. - Set benchmarks and success criteria for model performance. - Collaborate across AWS and Amazon to bring scientific breakthroughs to our customers. - Add your unique creativity to our multidisciplinary team. - Mentor junior scientists and interns/PhD students. We are open to hiring candidates to work out of one of the following locations: Haifa, ISR | Tel Aviv, ISR
US, WA, Seattle
Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the extreme. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. As a Applied Scientist at the intersection of machine learning and the life sciences, you will participate in developing exciting products for customers. Our team rewards curiosity while maintaining a laser-focus in bringing products to market. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the cutting edge of both academic and applied research in this product area, you have the opportunity to work together with a diverse and talented team of scientists, engineers, and product managers and collaborate with others teams. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, WA, Bellevue
As a Principal Research Scientist in the Amazon Artificial General Intelligence (AGI) Data Services organization, you will be responsible for sourcing and quality of massive datasets powering Amazon's AI. You will play a critical role in driving innovation and advancing the state-of-the-art in natural language processing and machine learning. You will be responsible for developing and implementing cutting-edge algorithms and techniques to extract valuable insights from large-scale data sources. You will work closely with cross-functional teams, including product managers, engineers, and data scientists to ensure that our AI systems are aligned with human policies and preferences. Key job responsibilities - Responsible for sourcing and quality of massive datasets powering Amazon's AI. - Collaborate with cross-functional teams to ensure that Amazon’s AI models are aligned with human preferences. - Develop and implement strategies to improve the efficiency and effectiveness of programs delivering massive datasets. - Identify and prioritize research opportunities that have the potential to significantly impact our AI systems. - Communicate research findings and progress to senior leadership and stakeholders. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Boston, MA, USA
US, WA, Redmond
Project Kuiper is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. We are searching for talented candidates with experience in spaceflight trajectory modeling and simulation, orbit mechanics, and launch vehicle mission planning. Key job responsibilities This position requires experience in simulation and analysis of astrodynamics models and spaceflight trajectories. Strong analysis skills are required to develop engineering studies of complex large-scale dynamical systems. This position requires demonstrated expertise in computational analysis automation and tool development. Working with the Kuiper engineering team, you will: - Develop modeling techniques for analysis and simulation of deployment dynamics of multiple satellites - Support Project Kuiper’s Launch Vehicle Mission Management team with technical expertise in Launch Vehicle trajectory requirements specification - Develop tools to support Mission Management planning for over 80 launches! - Work collaboratively with launch vehicle system technical teams Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. We are open to hiring candidates to work out of one of the following locations: Redmond, WA, USA