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There is a huge demand for deep learning jobs in big tech companies in 2022 and beyond

Deep learning jobs are in huge demand at multiple big tech companies to adopt digitalization and globalization in this global tech market. Yes, the competition is very high among big tech companies in recent times. Thus, they are offering deep learning vacancies with lucrative salary packages for experienced deep learning professionals. Machine learning jobs are also included in the vacancy list of big tech companies to apply for in April 2022. One can apply to these deep learning jobs if there is sufficient experience and knowledge about this domain. Hence, lets explore some of the top ten deep learning jobs in 2022 to look out for in big tech companies.

Location: Shanghai

Responsibilities: The architect must analyze the performance of multiple machine learning algorithms on different architectures, identify architecture and software performance bottlenecks and propose optimizations, and explore new hardware capabilities.

Qualifications: The candidate should have an M.S./Ph.D. in any technical field with sufficient experience in system architecture design, performance optimization, and machine learning frameworks.

Click here to apply

Location: California

Responsibilities: It is expected to research and implement novel algorithms in the artificial human domain while efficiently designing and conducting experiments to validate algorithms. One should help with the collection and curation of data, train models, and transform research ideas into high-quality product features.

Qualifications: They must be a Masters or Ph.D. in any technical field with hands-on experience in developing a product based on machine learning research, frameworks, programming languages, and many more.

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Location: North Reading

Responsibilities: The right candidate should develop deep neural net models, techniques, and complex algorithms for high-performance robotic systems. It is necessary to design highly scalable enterprise software solutions while executing technical programs.

Qualifications: There should have a Ph.D. in any technical field with more than two years of experience in a programming language, over three years in developing machine learning models and algorithms, and more than four years of research experience in this domain and machine learning technologies. It is necessary to have a strong record of patents and innovation or publications in top-tier peer-reviewed conferences.

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Location: Seoul

Responsibilities: It is expected to work on automatic speech recognition and keyword spotting with speech enhancement in a multi-microphone system. The researcher must be the representation of learning audio and speech data with generative models for speech generation or voice conversion.

Qualifications: There should be a deep knowledge of general machine learning, signal processing, speech processing, RNN, generative models, programming languages, and many more.

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Location: Bengaluru

Responsibilities: It is necessary to build innovative and robust real-life solutions for computer-vision applications in smart mobility and autonomous systems, develop strategic concepts and engage in technical business development, as well as solve challenges associated with transformation such large complex datasets.

Qualifications: The candidate must have a Ph.D./Masters degree in computer science with at least eight years of hands-on experience in computer vision, video analytics problems, training in deep convolutional networks, OpenCV, OpenGL, and many more.

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Location: Bengaluru

Responsibilities: The duties include enabling full-stack solutions to boost delivery and drive quality across the application lifecycle, performing continuous testing for security, creating automation strategy, participating in code reviews, and reporting defects to support improvement activities for the end-to-end testing process.

Qualifications: The engineer must have a Bachelors degree with eight to ten years of work experience with statistical software packages and a deep understanding of multiple software utilities for data and computation.

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Location: Santa Clara

Responsibilities: The duties include the analysis of the state-of-the-art algorithms for multiple computing hardware backends and utilizing experience with machine learning frameworks. There should be an implementation of multiple distributed algorithms with data flow-based asynchronous data communication.

Qualifications: The engineer must have a Masters/Ph.D. degree in any technical field with more than two years of industry experience.

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Location: Great Britain

Responsibilities: The scientist should develop novel algorithms and modelling techniques to improve state-of-the-art speech synthesis. It is essential to use Amazons heterogeneous data sources with written explanations and their application in AI systems.

Qualifications: The candidate should have a Masters or Ph.D. degree in machine learning, NLP, or any technical field with two years of experience in machine learning research projects. It is necessary to have hands-on experience in speech synthesis, end-to-end agile software development, and many more.

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Location: Bengaluru

Responsibilities: The candidate should work with programming languages like R and Python to efficiently complete the life cycle of a statistical modelling process.

Qualifications: The candidate must be a graduate or post-graduate with at least six years of experience in machine learning and deep learning.

Click here to apply

Location: Bengaluru

Responsibilities: It is essential to support the day-to-day activities of the development and engineering by coding and programming specifications by developing technical capabilities, assisting in the development and maintenance of solutions or infrastructures, as well as translating product requirements into technical requirements.

Qualifications: The candidate should have a B. Tech/M. Tech/MCA or a Bachelors degree in any technical field with more than three to five years of experience on SAP U15/ABAP/CDS/ and many more. It is essential to have sufficient knowledge of cloud development, maintenance process, SAP BTP services, and many more.

Click here to apply

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Top 10 Deep Learning Jobs in Big Tech Companies to Apply For - Analytics Insight

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Turk Neurosurg. 2021 Nov 10. doi: 10.5137/1019-5149.JTN.36068-21.3. Online ahead of print.

ABSTRACT

AIM: We sought to identify predictors of basilar invagination (BI) prognosis and compare diagnostic properties between logistic modeling and machine learning methods.

MATERIAL AND METHODS: We conducted a single-center retrospective study. Patients at our hospital who met the inclusion and exclusion criteria were identified between August 2015 and August 2020 for inclusion. Candidate predictors, such as demographics, clinical scores, radiographic parameters, and outcome, were included. The primary outcome was the prognosis evaluated by the change in patient-reported Japanese orthopaedic association (PRO-JOA) score. Conventional logistic regression models and machine learning algorithms were implemented. Models were compared, considering the area under the curve (AUC), sensitivity, specificity, positive and negative predictive values, and calibration curve.

RESULTS: Overall, the machine learning algorithms and traditional logistic regression models performed similarly. The postoperative cervicomedullary angle, head-neck flexion angle (HNFA), atlantodental interval, postoperative clivo-axial angle, age, postoperative clivus slope, postoperative cranial incidence, weight, postoperative HNFA, and postoperative Boogaards angle (BoA) were identified as important predictors for BI prognosis. Among the surveyed radiographic parameters, postoperative BoA was the most important predictor of BI prognosis. In the validation dataset, the bagged trees model performed best (AUC, 0.90).

CONCLUSION: Through machine learning, we have demonstrated predictors of BI prognosis. Machine learning methods did not provide too many advantages over logistic regression in predicting BI prognosis but remain promising.

PMID:35416266 | DOI:10.5137/1019-5149.JTN.36068-21.3

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Comparative Analysis Between Machine Learning Algorithms and Conventional Regression in Predicting the Prognosis of Patients with Basilar...

New Fuel Cycle integration enables organizations to capture more context from their known community members

SAN FRANCISCO, April 13, 2022--(BUSINESS WIRE)--UserTesting (NYSE: USER), a leader in video-based human insight, today announced new features as part of its quarterly product release to help companies gather human insights specifically tailored to how their business operates. Companies can now identify and customize interesting and relevant findings using their own common, corporate terminology. UserTesting has also rolled out usage management for workspaces, making it easier to plan and share testing capacity across the entire organization. Lastly, for teams that want to capture feedback from their Fuel Cycle community members, UserTesting now offers the ability to launch tests to these audiences directly from the UserTesting Human Insight Platform.

This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20220413005396/en/

Customize UserTesting Intent Path labels to better match the terminology used within organizations. (Graphic: Business Wire)

New features in this product release:

Customize auto-generated insightsUserTestings insight customization allows customers to provide feedback on auto-generated, intelligent insights by adding their own custom terminology. These insights are powered by machine learning and help surface intent, sentiment, keywords, and other key moments in a video. Customization is persistent and applied to future tests on similar experiences, helping to establish a common language for insights across the organization. This makes identifying key moments of interest faster and easier, and post-test analysis more efficient.

New integration with Fuel CycleUserTesting has expanded the ability for companies to access their own customer communities with a new Fuel Cycle integration. This integration allows companies to more efficiently reach and get feedback from their own customers that are already established via Fuel Cycle communities. Users can distribute tests from the UserTesting Human Insight Platform directly to Fuel Cycle community members, creating an easy and efficient way to capture feedback from these audiences.

Story continues

Usage management for workspacesWith usage management for workspaces, organizations can manage testing capacity across their enterprise more easily and efficiently. Customers can now expand the use of the UserTesting Human Insight Platform into new departments, groups, and lines of business while effectively managing testing capacity.

"UserTesting is continuously innovating its overall platform experience to bring greater efficiencies to how organizations collect, access, and take action on customer insights," said Kaj van de Loo, CTO at UserTesting. "The easier we can make it for organizations to capture these types of insights, the greater the customer intuition they can buildand its those companies that understand what is driving their customers behaviors that will be the market leaders."

About UserTestingUserTesting (NYSE: USER) has fundamentally changed the way organizations get insights from customers with fast, opt-in feedback and experience capture technology. The UserTesting Human Insight Platform taps into our global network of real people and generates video-based recorded experiences, so anyone in an organization can directly ask questions, hear what users say, see what they mean, and understand what its actually like to be a customer. Unlike approaches that track user behavior then try to infer what that behavior means, UserTesting reduces guesswork and brings customer experience data to life with human insight. UserTesting has more than 2,300 customers, including more than half of the worlds top 100 most valuable brands according to Forbes. UserTesting is headquartered in San Francisco, California. To learn more, visit http://www.usertesting.com.

View source version on businesswire.com: https://www.businesswire.com/news/home/20220413005396/en/

Contacts

UserTesting, Inc.Chris Halcon415-699-0553chalcon@usertesting.com

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UserTesting Announces New Capabilities that Provide Organizations with More Customizable and Powerful Machine Learning-Driven Insights - Yahoo Finance

A team of University of Iowa researchers received $800,000 from Amazon and the National Science Foundation to limit the discriminatory effects of machine learning algorithms.

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University of Iowa researcher Tianbao Yang seats at his desk where he works on AI research on Friday, Aril 8, 2022.

University of Iowa researchers are examining discriminative qualities of artificial intelligence and machine learning models, which are likely to be unfair against ones race, gender, or other characteristics based on patterns of data.

A University of Iowa research team received an $800,000 grant funded jointly by the National Science Foundation and Amazon to decrease the possibility of discrimination through machine learning algorithms.

The three-year grant is split between the UI and Louisiana State University.

According to Microsoft, machine learning models are files trained to recognize specific types of patterns.

Qihang Lin, a UI associate professor in the department of business analytics and grant co-investigator, said his team wants to make machine learning models fairer without sacrificing an algorithms accuracy.

RELATED: UI professor uses machine learning to indicate a body shape-income relationship

People nowadays in [the] academic field ladder, if you want to enforce fairness in your machine learning outcome, you have to sacrifice the accuracy, Lin said. We somehow agree with that, but we want to come up with an approach that [does] trade-off more efficiently.

Lin said discrimination created by machine learning algorithms is seen disproportionately predicting rates of recidivism a convicted criminals tendency to re-offend for different social groups.

For instance, lets say we look at in U.S. courts, they use a software to predict what is the chance of recidivism of a convicted criminal and they realize that that software, that tool they use, is biased because they predicted a higher risk of recidivism of African Americans compared to their actual risk of recidivism, Lin said.

Tianbao Yang, a UI associate professor of computer science and grant principal investigator, said the team proposed a collaboration with Netflix to encourage fairness in the process of recommending shows or films to users.

Here we also want to be fair in terms of, for example, users gender, users race, we want to be fair, Yang said. Were also collaborating with them to use our developed solutions.

Another instance of machine learning algorithm unfairness comes in determining what neighborhoods to allocate medical resources, Lin said.

RELATED: UI College of Engineering uses artificial-intelligence to solve problems across campus

In this process, Lin said the health of a neighborhood is determined by examining household spending on medical expenses. Healthy neighborhoods are allocated more resources, creating a bias against lower income neighborhoods that may spend less on medical resources, Lin said.

Theres a bad cycle that kind of reinforces the knowledge the machines mistakenly have about the relationship between the income, medical expense in the house, and the health, Lin said.

Yao Yao, UI third-year doctoral candidate in the department of mathematics, is conducting various experiments for the research team.

She said the importance of the groups focus is that they are researching more than simply reducing errors in machine learning algorithm predictions.

Previously, people only focus on how to minimize the error but most time we know that the machine learning, the AI will cause some discrimination, Yao said. So, its very important because we focus on fairness.

Read more:
Amazon awards grant to UI researchers to decrease discrimination in AI algorithms - UI The Daily Iowan

Now landing your favorite ML job at Tesla might be closer than you think! Read to learn more.

If working at Tesla is one of your dream jobs then heres your chance. Tesla is hiring artificial intelligence and machine learning engineers and if you think you have done exceptional work in software, hardware, or AI then you may directly apply with your CV by visiting the official Tesla website. While you may be wondering what qualifies to be called exceptional work then the CEO, Elon Musk himself has provided a tip. In one of his tweets, Musk said, As always, Tesla is looking for hardcore AI engineers who care about solving problems that directly affect peoples lives in a major way.

Tesla has scope for machine learning engineers who are interested in working in developing Full Self-Driving (FSD) chips, Dojo systems, Neural Networks, Autonomy Algorithms, and coding. If given a chance you may be part of the Tesla Bot project as well. In another tweet, Elon Musk clarified that he is fine with hiring people without AI background too. A background in AI is not needed, just exceptional skill in software or computer design, he said. On the other hand, the Tesla website job posting has described that the company is looking for mechanical, electrical, controls, and software engineers to help leverage its AI expertise beyond the vehicle fleet.

For hardcore coders, Tesla wants engineers who can build Autopilot software foundations up from the lowest levels of the stack, tightly integrating with Teslas custom hardware. The company aims to Implement super-reliable bootloaders with support for over-the-air updates and bring up customized Linux kernels. It wants the ML engineers to write fast, memory-efficient low-level code to capture high-frequency, high-volume data from the sensors, and to share it with multiple consumer processes without impacting central memory access latency or starving critical functional code from CPU cycles.

Another special job related to machine learning in Tesla is Autopilot Simulation Deep Learning Engineers. Autopilot is of critical importance to Teslas mission. It is safer, makes driving more enjoyable, and will ultimately deliver on the promise of self-driving cars. As a member of Teslas Autopilot Simulation team, you will be in a unique position to accelerate the pace at which Autopilot improves over time. The Simulation team realizes these goals through generating synthetic datasets for neural network training, building tools that enable Autopilot software developers to perform virtual test drives instead of real ones, and through testing all Autopilot code changes and software releases for regressive behavior.

As an Autopilot Simulation Deep Learning Engineer, you will contribute to the development of Autopilot simulation by enabling and accelerating the creation of photorealistic 3D scenes through neural rendering, neural animation, and scene/object reconstruction. You will be at the cutting edge of deep learning applications and will train neural networks on a cluster in large-scale distributed settings as Tesla has one of the biggest training clusters in the world. You will have tremendous reach and autonomy, with an exciting opportunity to impact Teslas goal of full self-driving and its mission to accelerate the worlds transition to sustainable energy. As the machine learning engineer, you will be a creative self-starter with an ability to self-assign projects across different areas of the Autopilot team. You will also have the opportunity to identify gaps between real and simulated environments, and then you will get to close those gaps through state-of-the-art machine learning/deep learning methods.

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Want to Land Your Dream ML Job at Tesla? Here's How? - Analytics Insight