Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

The Amazon Web Services (AWS) team is seeking an innovation and results-oriented Software Development Engineer in our Textract team. Amazon Textract (https://aws.amazon.com/textract/) is a new AWS service that launched in Nov. 2018 and provides deep learning-based document image analysis. The engineering team owns the full service stack and solves challenging problems around scaling, efficiency, and reliability to make sure our customers discover content they care about. We are constantly adding new capabilities to the service. The goal is to be the bleeding edge of a highly scalable technology that can analyze billions of images daily and provide rich analytics.

Tiffany Ceasor works for Microsoft in Seattle, where she is in the process of transitioning from a data scientist role to becoming a software development engineer with a focus on data science. She is a student at Florida Atlantic University, working towards her PhD in computer science with a specialism in applied artificial intelligence. Tiffany also studied at Florida Atlantic for her bachelor's degree in international public health and public health before taking her master of science in management information systems and business analytics there too. "Diversity in thought, culture, personality, life experiences and beliefs are what make the world an incredible, dynamic and beautiful place. In technology it is especially important because, as artificial intelligence advances, it is imperative to prevent eminent biases from being trained into models that will in turn potentially determine the future of the way we do things."

Amazon SageMaker Autopilot automatically builds, trains, and tunes the best machine learning (ML) models based on your data, while allowing you to maintain full control and visibility. We have recently announced support for time series data in Autopilot. You can use Autopilot to tackle regression and classification tasks on time series data, or sequence data in general. Time series data is a special type of sequence data where data points are collected at even time intervals. Manually preparing the data, selecting the right ML model, and optimizing its parameters is a complex task, even for an expert practitioner.

Industry-focused Certifications: We want you to be a subject matter expert in what you do. So, whether it's our product or our domain, you will dive straight in and be certified by the best in the world. Quarterly Rewards and Recognition Programs: We foster learning and encourage people to take moonshots. When you achieve your goals, we recognize and reward your hard work. Health Benefits: We cover health insurance for you and your loved ones.

Are you interested in building high-performance, globally scalable Financial systems that support Amazon's current and future growth? Are you seeking an environment where you can drive innovation? Does the prospect of working with top engineering talent get you charged up? If so, Amazon's Finance Technology (FinTech) is for you! As a Software Development Engineer in FinTech Treasury, you will build real time financial calculation engines and reports to assess Amazon's exposure to financial risks, and use Machine Learning (ML) to forecast cash balances across the company.

Examples include IBM Watson, Stanford's DeepDive or Google Prediction. Keysight Technologies Inc. is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability or any other protected categories under all applicable laws.

Amazon in late April announced that it would expand its tech hub in Vancouver, planning to hire 3,000 engineers with experience in cloud computing and machine learning. The company also announced plans to add 2,000 tech jobs in the Boston area, focused on improving the company's Alexa and Audible technologies. Amazon Web Services in June announced it would be adding 1,000 engineers to its Dublin, Ireland, offices over the next two years, including software development engineers, network development engineers, security and big-data specialists, and development operations engineers. Walmart in June told VentureBeat that it would be adding 2,000 tech workers to its Walmart Labs operations in Silicon Valley; Bentonville, Arkansas; Reston, Virginia; and Bangalore, India, with many focused on using machine learning to improve its online shopping tools. PayPal India announced in July that it would be hiring 600 additional engineers this year, including application developers, back-end developers, data engineers, and product managers.

Until the last 5 years or so, it was infeasible to uncover topics and emotions across the web without powerful computing resources. Engineers didn't have efficient methods to make sense of words and documents at a large scale. Now, with deep learning, we can convert unstructured text to computable formats, effectively incorporating semantic knowledge for training machine learning models. Harnessing the vast data troves of the digital world can help us understand people more directly, going beyond the limitations of collecting data points through measurements and survey results. Here's a glimpse into how we achieve this at MarianaIQ.

DESCRIPTION Online fashion shopping is a multi-billion industry which is growing exponentially year over year. Does the problem space of providing an awesome online experience for customer without the typical touch and feel retail experience thrill you? Are you excited by the challenge of building large scale systems that will be used by millions of customers, day-in-day-out? If the answer is yes, we would like you to take a look at the Amazon Fashion Technology team, which is currently solving this problem. Experiences like which-size-fits-me, what-goes-well-with-this are huge challenges to solve in an online space.