With digitalization and globalization, there is a huge demand for deep learning jobs in big tech companies
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 July 2022. One can apply to these deep learning jobs if there is sufficient experience and knowledge about this domain. Hence, let’s explore some of the top 10 deep learning jobs to apply for in July.
Applied research scientist, computer vision/deep learning-NEON
Samsung Research America
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 collect and curate data, train models, and transform research ideas into high-quality product features.
Qualifications:
They must be a Master’s 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.
Applied scientist- machine learning/deep learning
Amazon
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:
They 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.
Deep Learning Solution Architect
NVIDIA
Responsibilities:
Assist field business development in guiding the customer through the sales process for GPU Computing products, owning the technical relationship, and assisting customers in building innovative solutions based on NVIDIA technology. Be an industry thought leader in integrating NVIDIA technology into HPC architectures to support Scientific and engineering applications. Be an internal champion for Deep Learning or Data Science among the NVIDIA technical community.
Deep learning researcher- speech recognition
Qualcomm
Responsibilities:
It is expected to work on automatic speech recognition and keyword spotting with speech enhancement in a multi-microphone system. The researcher must represent 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.
Deep learning quality engineer (tester)
Accenture
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 Bachelor’s 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.
Deep Learning Software Engineer
Intel
Responsibilities:
Analyze deep learning networks and framework implementations to identify performance bottlenecks and optimization opportunities. Develop high-performance and highly parallel software kernel implementations for GPUs together with the math library team. Explore and implement various distributed algorithms such as model/data-parallel frameworks. Work with a government partner to analyze their applications and understand how they use deep learning and where optimization is needed.
Deep Learning Software Engineer
Amazon
Qualifications:
The candidate should have a Master’s 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.
Machine learning and deep learning
WNS
Responsibilities:
The candidate should work with programming languages like R and Python to efficiently complete the life cycle of a statistical modeling process.
Qualifications:
The candidate must be a graduate or post-graduate with at least six years of experience in machine learning and deep learning.
Data Science Engineer – Machine/Deep Learning Models – R/Python/Scala
CarbyneTech India
Responsibilities:
Review deployments, source codes, configurations, algorithms, models, and repositories for completeness. Identify missing libraries/codes, dependencies, and code documentation. Update technical documentation, code comments, etc. Integrate the code into DevOps.
Technical Expert: Deep Learning & Computer Vision
Siemen
Requirements:
- Minimum 6 years of experience working on Image processing, Computer vision, and Video Analytics problems with a clear understanding and ability to implement algorithms (especially deep learning algorithms)
- Solid hands-on experience in training deep convolutional and/or recurrent networks using frameworks like Tensorflow, Caffe, and PyTorch
- Hands-on experience using OpenCV and OpenGL
- Experience with datasets such as Visual Genome for applications in image description and answering questions
Source: analyticsinsight.net
