Texas software engineer

Most common benefits Cash bonus. Is this useful? View job openings with the years of experience that is relevant to you on Indeed. Top companies for Software Engineers in Texas. Apple 4. Samsung Electronics 4. Octo Consulting Group 3. Juniper Networks 4. PayPal 3. Show more companies Show more companies. Where can a Software Engineer earn more?

Compare salaries for Software Engineers in different locations Search Location. Search Location. Compare Explore Software Engineer openings. Best-paid skills and qualifications for Software Engineers award Most recommended skill. Top companies hiring Software Engineers with the recommended skill.

View more companies for Software Engineers. Salaries by qualifications. Fields of study Skills. Show More. Most common benefits for Software Engineers. Stock options Visa sponsorship Referral program Commuter assistance. Food provided Relocation assistance Gym membership k.

Health savings account Health insurance k matching Vision insurance. How much do similar professions get paid in Texas? Developer 17, job openings. Full Stack Developer 2, job openings. Application Developer 11, job openings. Would you thrive in a world class engineering organization that emphasizes providing exciting work opportuniti Are you an Data Engineer multiple openings.

Roku, Inc. Reqs incl. Email resume to resumes roku. Does solving unique problems energize you? Do you like to create distinctive solutions? Learn more about the differences and similarities of these programs by exploring the articles under the "Further Reading" section to the right of this page.

All Rights Reserved. What is Software Engineering? Software Engineering vs. Computer Science Software Engineering vs. EE C Mobile Computing As mobile computing devices like laptops, PDAs, cellular phones, and even miniature sensors become increasingly pervasive, the demand for applications for this novel environment escalates. This course explores the effects of mobile computing on software design and development. The approach taken uses current research projects in the field of mobile computing to highlight the key aspects that complicate software engineering.

We will focus on these concerns in the context of application development. It assume undergraduate knowledge of sequential algorithms.

The following topics will be covered in the course:. The course will start with looking at tools like compliers, linkers, loaders, and debuggers that an operating system provides and how they work. The second part of the class is on the design and implementation of an operating system with focus on process, memory virtualization, and concurrency.

EE V Formal Methods in Distributed Systems This course gives an introduction to the use of formal methods within the software design process. Specifically, this class will cover the application of models to distributed and concurrent systems. Modern software systems are commonly highly distributed, and this added sophistication further complicates software design.

The rigor offered by formal methods aims to make the process more precise. Using the right tools can solve these problems. Examples include tools for version control, documentation, program building and configuration, automatic testing, program analysis, and integrated development.

Our approach will be to introduce a specific problem, show how a tool can solve the problem, and then develop the technical principles underlying the tool. We will have written homework problems as well as coding exercises for each concept. The class will have a major design project that will begin at the start of the term.

Use of the tools will be a required part of the project. We will use open-source tools to illustrate these concepts.

The specific tool stack is described in the lectures section of this document. I selected these tools based on my experience at Google; they also power many state-of-the-art commercial projects. Issues of interest include database design; meta knowledge of the data and its processing; languages to describe data, define access, and manipulate databases; strategies and mechanisms for data access, security, and integrity control.

The emphasis of this course is on algorithms where multiple agents interact with each other. EE V Software Testing This course first introduces the basics of software testing theory and practice, and then presents some recently developed techniques for systematically finding bugs in programs and improving their reliability. Feedforward neural networks. Convolutional neural networks. Deep learning training by backpropagation. Interpretability of deep neural networks.

Prediction and overfitting. Statistical Learning theory. Unsupervised machine learning. Deep generative unsupervised models. Generative Adversarial networks and Autoencoders. Applied deep learning using Python, Tensorflow and Keras. EE L Data Mining Basic concepts of data mining, in parallel with a practical track involving hands-on experience with industrial strength software and a term project will be covered.

It covers all basic components of modern networks, including: link level technologies such as Ethernet, token rings, and wireless Ethernet; switching technologies such as bridges and ATM; internetworking including IP; the transport layer, including TCP and RPC; and congestion control.

EE N. EE C System Engineering Program Management and Evaluation Management, engineering, and evaluation approaches applicable to a spectrum of software development programs is taught. General guidelines, metrics, program artifacts, and processes will be discussed in conjunction with case studies.

EE Computer Graphics This is an introductory course on the major topics in computer graphics including image synthesis, interactive techniques, geometric modeling, and computer-based animation. Covered material includes: OpenGL programming, principles of operation of raster graphics systems, sampling and antialiasing, homogeneous coordinate transformation techniques, parallel and central projection and perspective transformations, hidden surface removal, light and reflectance models for local and global illumination, shading techniques, ray tracing, basic object modeling techniques, visual perception and basic color theory, hierarchical modeling, and basic animation.

EE Middleware This course is a graduate level course introducing and investigating middleware at all levels, largely from a software engineering perspective.

Students are introduced to various types of middleware from object-oriented middleware to message-oriented middleware and beyond both through lecture materials and through active "mini-projects" through which the students build complex applications using existing middleware solutions. The course also offers lectures on "trends" in middleware, including how middleware addresses challenges related to mobile computing, sensor networks, real-time computing, "green computing," etc.

We will then study measuring program performance using the big-O notation. Following this, we will study fundamental data structures and their associated algorithms; specifically, we will cover lists, arrays, queues, stacks, hash tables, sets, binary trees, and graphs.

We will then focus on general algorithm design principles, such as greedy approaches and dynamic programming. Our last topic will be matrix algorithms. The principle focus of the lectures will be on theoretical aspects, in the style of the CLRS Algorithms text listed below. There will also be a number of programming assignments that will require implementing and testing algorithms.

In addition, there will be a team project that either evaluates some textbook algorithm s in real-world settings, or explores how to specialize and enhance some textbook algorithm s under specific conditions.

EE V Introduction to Optimization This course will serve as an introduction to modeling, applications and algorithms of discrete and continuous optimization.