Deep Learning Engineer

A Deep Learning Engineer develops and implements neural networks and machine learning algorithms to enable machines to learn and solve complex problems.
Salary Insights
High-ROI Certifications
Potential Lateral Jobs
Publications/ Groups

Deep Learning Engineer

National (USA)
Base Salary
$164,716 / year
Additional Benefits
Bachelor's Degree

The average salary for Deep Learning Engineer is $164,716 / year according to

There are no updated reports for Deep Learning Engineer salaries. You can check potential lateral job opportunities in this information stack to find related salary information.

Deep Learning Engineer role may have an alternate title depending on the company. To find more information, you can check

Career Information

As a Deep Learning Engineer, you will be responsible for designing and implementing deep learning models for organizations. You will need a strong understanding of machine learning algorithms and frameworks, as well as experience with programming languages like Python or TensorFlow. Strong problem-solving and analytical skills are essential, as you will be responsible for developing and optimizing deep learning models.

The average salary for Deep Learning Engineer is $164,716 / year according to
AI Disclaimer
The following text about the Job role of Deep Learning Engineer has been generated by an AI model developed by Cohere. While efforts have been made to ensure the accuracy and coherence of the content, there is a possibility that the model may produce hallucinated or incorrect information. Therefore, we strongly recommend independently verifying any information provided in this text before making any decisions or taking any actions based on it.

A Deep Learning Engineer is a specialized computer engineer who focuses on the design and development of artificial neural networks. These networks are used to process and analyze large amounts of data, and they are particularly well-suited for tasks such as image recognition, natural language processing, and speech recognition.

The most important skills for a Deep Learning Engineer include a strong understanding of computer programming, particularly in Python, C++, or Java. They must also have a solid foundation in statistics and mathematics, including algebra, calculus, and probability theory.

In addition, a Deep Learning Engineer must have experience with machine learning algorithms and frameworks, such as TensorFlow, PyTorch, or MXNet. They must also be familiar with the latest trends in artificial intelligence and be able to apply them to their work.

Some of the tasks that a Deep Learning Engineer may be responsible for include:

  • Designing and developing artificial neural networks for a wide range of applications, such as image recognition, natural language processing, and speech recognition
  • Training and testing these networks on large amounts of data
  • Optimizing the performance of these networks through techniques such as hyperparameter tuning and transfer learning
  • Deploying and maintaining these networks in production environments
  • Collaborating with other engineers and scientists to develop new machine learning algorithms and applications

Overall, a Deep Learning Engineer is a highly skilled and specialized computer engineer who plays a crucial role in the development of artificial intelligence and machine learning technologies.

Potential Lateral Jobs
Explore the wide range of potential lateral job opportunities and career paths that are available in this role.

High-ROI Programs

Most roles require at least a bachelor's degree. To remain competitive, job seekers should consider specialization or skill-specific programs such as specialization, bootcamps or certifications.
Certification Programs
Consider pursuing specialized certifications or vendor-specific programs to enhance your qualifications and stand out in the job market.

Microsoft Certified: Azure AI Engineer Associate


The Azure AI Certification is a high-ROI program designed for professionals who are passionate about building, managing, and deploying AI solutions using Azure Cognitive Services and Azure services.

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AWS Certified Machine Learning — Specialty Certification


The AWS Certified Machine Learning - Specialty certification covers a wide range of topics, including data engineering, exploratory data analysis, modeling, and machine learning implementation and operations on the AWS Cloud. 

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Google Certified Professional Machine Learning Engineer

The Google Machine Learning Certification is a high-ROI program designed for ML engineers who want to gain specialized machine learning skills using Google Cloud technologies.

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AWS Certified Data Analytics — Specialty


The AWS Data Analytics Certification program validates a deep understanding of AWS data analytics services and their integration with each other to derive insights from data, making it suitable for individuals pursuing a role focused on data analytics.

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Microsoft Certified: Azure Data Scientist Associate


The Microsoft Certified: Azure Data Scientist Associate is a high-ROI program designed for professionals who have expertise in applying data science and machine learning techniques to implement and manage machine learning workloads on Azure.

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Specialty Courses improving
If you want to improve your skills and knowledge in a particular field, you should think about enrolling in a Nanodegree or specialization program. This can greatly improve your chances of finding a job and make you more competitive in the job market.

Deep Learning in TensorFlow

Skill Track

With guided projects based on realistic business scenarios, you'll build a strong portfolio and be well-prepared for interviews.

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Machine Learning Scientist with Python

Career Track

Master key machine learning skills with 23 concise courses on Python, supervised & unsupervised learning, NLP, TensorFlow, PyTorch, Keras, and more for a successful career.

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Machine Learning Scientist with R

Career Track

Boost your AI career as a respected Machine Learning Scientist with this comprehensive program, enhancing your statistical programming skills and setting you apart from peers.

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Machine Learning Engineering for Production (MLOps) Specialization


This MLOps Specialization offers an in-depth understanding of creating, deploying, and maintaining integrated systems, managing data changes, and optimizing performance.

Machine Learning Specialization


Master fundamental AI concepts and practical machine learning skills through this high-ROI specialization taught by AI visionary Andrew Ng.

Machine Learning DevOps Engineer - Nanodegree


Master DevOps skills for automating ML model building & monitoring with this Nanodegree program, offering technical mentorship for aspiring MLOps/ML DevOps engineers.

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Practicing Machine Learning Interview Questions in Python


Natural Language Processing Specialization


IBM Applied AI Professional Certificate


Mathematics for Machine Learning and Data Science Specialization


Self Driving Car Engineer


How to Become a Robotics Engineer


Intro to Self-Driving Cars


Introduction to Data Science


Deep Learning Specialization


Resource Stacks

We are soon crowdsourcing these resource stacks to collate the best resources, such as publications, community groups, job boards, etc., that are practically suitable for every contextual stack.
Discover the wide array of publications that professionals in this role actively engage with, expanding their knowledge and staying informed about the latest industry trends and developments.
Communities updating
Discover the thriving communities where professionals in this role come together to exchange knowledge, foster collaboration, and stay at the forefront of industry trends.
Research updating
We are currently in the process of updating contextual resources and we will be adding the new ones to the list shortly.
AI Disclosure: We are testing AI technologies to ensure the accuracy and coherence of recommendations. However, it is important to note that there is a possibility that the model may create hallucinated or incorrect inferences. Therefore, we highly recommend independently verifying any information provided in these stacks before making any decisions or taking any actions based on it.

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