About the team
Zillow Offers seeks to give consumers certainty and control when selling their home. With the Zillow Offers experience, Zillow buys homes directly from interested homeowners, thereby sparing them the tremendous stress and effort of selling. Instead, we take that upon ourselves by quickly preparing these homes for the market and re-selling them. This is a key initiative at Zillow as we seek to reinvent real estate transactions.
Buying and selling homes is a capital intensive operation. We are responsible for building a technology platform to enable us to efficiently access, manage, and optimize our use of capital. Come join us as we leverage operational research and machine learning to introduce automated decision making and portfolio balancing.
About the role
Zillow embraces daunting challenges like this one, and that’s why we are looking for a Senior Machine Learning Engineer to help lead our success. Zillow Offers is still in very early stages, so come seize this rare opportunity to make a significant impact. You will be joining a passionate team of developers who are building Zillow into the biggest real-estate marketplace online. We believe in autonomy and moving fast, so self-motivation and the energy to blaze trails is a must-have for this role.
As a Senior member of this team you will:
- Lead the design, architecture and implementation of new machine learning and operational research models.
- Iterate quickly and ship code frequently.
- Work as a part of a team to define the long term technology vision.
- Set best practices influencing not only the team, but the entire Zillow Organization.
- Research and pioneer the adoption and use of new technologies within Zillow.
- Be willing to apply your experience to problems at every level of the tech stack.
- Champion operational excellence with a dedication to outstanding, reliable, and easy to maintain product.
Who you are
- Bachelors or Master’s degree in Computer Science or related field.
- Experience building, validating, and operating machine learning models.
- Expertise building and scaling resilient backend services.
- Dedication to software quality and operational excellence.
- Strong engineering judgement, enabled by depth and breadth of knowledge and experience.
- Collaborative working style with a strong focus on execution and results.
- Curious about new technologies and possess a growth mentality.
- Iterative approach to software development combined with the dedication to move fast and think big.
- Experience with AWS, GraphQL, Machine Learning, and/or Mixed Integer Programming a plus.
Get to know us
Zillow is the leading real estate and rental marketplace dedicated to empowering consumers with data, inspiration and knowledge around the place they call home, and connecting them with the best local professionals who can help. Zillow is part of Zillow Group, whose mission is to build the largest, most trusted and vibrant home-related marketplace in the world.
At Zillow Group, we’re powered by our inclusive work culture, where everyone has the support and resources to do the best work of their careers. Our efforts to streamline the real estate transaction is supported by our passion to empower people and enrich lives around everything home, a deep-rooted culture of innovation, a fundamental commitment to Equity and Belonging, and world-class benefits. But, don’t just take our word for it. Read our reviews on Glassdoor and recent recognition from multiple organizations, including: Fortune 100 Best Companies to Work For (#69), Fortune Best Workplaces for Diversity (#38), Fortune Best Workplaces for Parents (#31), Fortune Best Workplaces for Women (#20), Fatherly’s Best Workplaces for New Dads (#37), JUST Capital 100 Company (#69), Bloomberg Gender Equality Index constituent.
Zillow Group is an equal opportunity employer committed to fostering an inclusive, innovative environment with the best employees. Therefore, we provide employment opportunities without regard to age, race, color, ancestry, national origin, religion, disability, sex, gender identity or expression, sexual orientation, or any other protected status in accordance with applicable law. If there are preparations we can make to help ensure you have a comfortable and positive interview experience, please let us know.
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What’s behind the machine learning hype? In this non-technical course, you’ll learn everything you’ve been too afraid to ask about machine learning. There’s no coding required. Hands-on exercises will help you get past the jargon and learn how this exciting technology powers everything from self-driving cars to your personal Amazon shopping suggestions. How does machine learning work, when can you use it, and what is the difference between AI and machine learning? They’re all covered. Gain skills in this hugely in-demand and influential field, and discover why machine learning is for everyone!
This course will introduce the key elements of machine learning to the business leaders. We will focus on the key insights and base practices how to structure business questions as modeling projects with the machine learning teams. You will understand the different types of models, what kind of business questions they help answer, or what kind of opportunities they can uncover, also learn to identify situations where machine learning should NOT be applied, which is equally important. You will understand the difference between inference and prediction, predicting probability and amounts, and how using unsupervised learning can help build meaningful customer segmentation strategy.
Edureka’s Deep Learning in TensorFlow with Python Certification Training is curated by industry professionals as per the industry requirements & demands. You will master the concepts such as SoftMax function, Autoencoder Neural Networks, Restricted Boltzmann Machine (RBM) and work with libraries like Keras & TFLearn. The course has been specially curated by industry experts with real-time case studies.
Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks
A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures
Understand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networks
Learn the mathematical concepts required to understand how deep learning models function
Use deep learning for problems related to vision, image, text, and sequence applications
Time Series is an exciting and important part of Data Analysis. Time Series Data is more readily available than most forms of data and answers questions that cross-sectional data struggle to do. It also has more real world application in the prediction of future events. However it is not generally found in a traditional data science toolkit. There is also limited centralized resources on the applications of Time Series, especially using traditional programming languages such as Python.
The AI-Powered Enterprise: Harness the Power of Ontologies to Make Your Business Smarter, Faster, and More Profitable
Learn how to develop and employ an ontology, the secret weapon for successfully using artificial intelligence to create a powerful competitive advantage in your business.
The AI-Powered Enterprise examines two fundamental questions: First, how will the future be different as a result of artificial intelligence? And second, what must companies do to stake their claim on that future?
Deep learning is revolutionizing how machine translation systems are built today. This book introduces the challenge of machine translation and evaluation – including historical, linguistic, and applied context — then develops the core deep learning methods used for natural language applications. Code examples in Python give readers a hands-on blueprint for understanding and implementing their own machine translation systems. The book also provides extensive coverage of machine learning tricks, issues involved in handling various forms of data, model enhancements, and current challenges and methods for analysis and visualization. Summaries of the current research in the field make this a state-of-the-art textbook for undergraduate and graduate classes, as well as an essential reference for researchers and developers interested in other applications of neural methods in the broader field of human language processing.
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book’s web site.
Hands-On One-shot Learning with Python: Learn to implement fast and accurate deep learning models with fewer training samples using PyTorch
Get to grips with building powerful deep learning models using PyTorch and scikit-learn
Learn how you can speed up the deep learning process with one-shot learning
Use Python and PyTorch to build state-of-the-art one-shot learning models
Explore architectures such as Siamese networks, memory-augmented neural networks, model-agnostic meta-learning, and discriminative k-shot learning
Cybernetics, Cognition and Machine Learning Applications: Proceedings of ICCCMLA 2019 (Algorithms for Intelligent Systems)
This book provides a collection of selected papers presented at the International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA 2019), which was held in Goa, India, on 16–17 August 2019. It covers the latest research trends and advances in the areas of data science, artificial intelligence, neural networks, cognitive science and machine learning applications, cyber-physical systems, and cybernetics.
Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter: Build scalable real-world projects to implement end-to-end neural networks on Android and iOS
Learn how to deploy effective deep learning solutions on cross-platform applications built using TensorFlow Lite, ML Kit, and Flutter
Work through projects covering mobile vision, style transfer, speech processing, and multimedia processing
Cover interesting deep learning solutions for mobile
Build your confidence in training models, performance tuning, memory optimization, and deploying neural networks through every project
Machine learning is going to be something that you are going to use so that you can discover why and how you are getting the outcomes that you are getting with the program that you are using.
With machine learning, you are going to have the option of putting the data in that you want into the program and getting the results that you want to get. You are going to better understand where you made a mistake so that you can go back in and fix it.