Ops Integration: Concessions team is looking for a motivated, creative and customer obsessed Snr. Applied Scientist with a strong machine learning background, to develop advanced analytics models (Computer Vision, LLMs, etc.) that improve customer experiences We are the voice of the customer in Amazon’s operations, and we take that role very seriously. If you join this team, you will be a key contributor to delivering the Factory of the Future: leveraging Internet of Things (IoT) and advanced analytics to drive tangible, operational change on the ground. You will collaborate with a wide range of stakeholders (You will partner with Research and Applied Scientists, SDEs, Technical Program Managers, Product Managers and Business Leaders) across the business to develop and refine new ways of assessing challenges within Amazon operations. This role will combine Amazon’s oldest Leadership Principle, with the latest analytical innovations, to deliver business change at scale and efficiently The ideal candidate will have deep and broad experience with theoretical approaches and practical implementations of vision techniques for task automation. They will be a motivated self-starter who can thrive in a fast-paced environment. They will be passionate about staying current with sensing technologies and algorithms in the broader machine vision industry. They will enjoy working in a multi-disciplinary team of engineers, scientists and business leaders. They will seek to understand processes behind data so their recommendations are grounded. Key job responsibilities Your solutions will drive new system capabilities with global impact. You will design highly scalable, large enterprise software solutions involving computer vision. You will develop complex perception algorithms integrating across multiple sensing devices. You will develop metrics to quantify the benefits of a solution and influence project resources. You will validate system performance and use insights from your live models to drive the next generation of model development. Common tasks include: • Research, design, implement and evaluate complex perception and decision making algorithms integrating across multiple disciplines • Work closely with software engineering teams to drive scalable, real-time implementations • Collaborate closely with team members on developing systems from prototyping to production level • Collaborate with teams spread all over the world • Track general business activity and provide clear, compelling management reports on a regular basis Basic Qualifications -Masters in Computer Science, Machine Learning, Robotics or equivalent with a focus on Computer Vision. -2+ years of experience of building machine learning models for business application -Broad knowledge of fundamentals and state of the art in computer vision and machine learning -Strong coding skills in two or more programming languages such as Python or C/C++ -Knowledge of fundamentals in optimization, supervised and reinforcement learning -Excellent problem-solving ability Preferred Qualifications -PhD and 4+ years of industry or academic applied research experience applying Computer Vision techniques and developing Computer vision algorithms -Depth and breadth in state-of-the-art computer vision and machine learning technologies and experience designing and building computer vision solutions -Industry experience in sensor systems and the development of production computer vision and machine learning applications built to use them -Experience developing software interfacing to AWS services -Excellent written and verbal communication skills with the ability to present complex technical information in a clear and concise manner to a variety of audiences -Ability to work on a diverse team or with a diverse range of coworkers -Experience in publishing at major Computer Vision, ML or Robotics conferences or Journals (CVPR, ICCV, ECCV, NeurIPS, ICML, IJCV, ICRA, IROS, RSS,...) We are open to hiring candidates to work out of one of the following locations: London, GBR
October 20, 2023Method enables machine-checkable proofs of SAT solvers’ decisions on incremental SAT problems, in which problem constraints are gradually imposed over time.
