Deep Learning Engineer Salary in 2023

Salary details

The average Deep Learning Engineer salary lies between USD 120,000 and USD 303,387 globally. It represents the overall compensation/gross salary amount for the working year (before deductions like social security, taxes and other contributions), not including equity/stock options or similar benefits.

All data shown are full-time equivalent (FTE) salaries. Part-time salary information has been extrapolated to its FTE value.

Last updated: Dec. 1, 2024

Top 20 Job Tags for Deep Learning Engineer roles

The three most common job tag items assiciated with Deep Learning Engineer job listings are Deep Learning, Research and Machine Learning. Below you find a list of the 20 most occuring job tags in 2023 and the number of open jobs that where associated with them during that period:

Deep Learning | 33 jobs Research | 27 jobs Machine Learning | 25 jobs TensorFlow | 24 jobs PyTorch | 23 jobs Python | 20 jobs Computer Vision | 20 jobs PhD | 19 jobs Architecture | 18 jobs Computer Science | 16 jobs Engineering | 15 jobs Keras | 12 jobs Pipelines | 12 jobs Testing | 8 jobs CUDA | 6 jobs Mathematics | 6 jobs NLP | 5 jobs Autonomous Driving | 5 jobs R | 4 jobs GPU | 4 jobs

Top 20 Job Perks/Benefits for Deep Learning Engineer roles

The three most common job benefits and perks assiciated with Deep Learning Engineer job listings are Career development, Equity / stock options and Competitive pay. Below you find a list of the 20 most occuring job perks or benefits in 2023 and the number of open jobs that where offering them during that period:

Career development | 20 jobs Equity / stock options | 16 jobs Competitive pay | 12 jobs Flex hours | 11 jobs Team events | 10 jobs Health care | 8 jobs Flex vacation | 7 jobs Startup environment | 7 jobs Salary bonus | 6 jobs Unlimited paid time off | 6 jobs Conferences | 5 jobs 401(k) matching | 4 jobs Lunch / meals | 4 jobs Insurance | 4 jobs Parental leave | 2 jobs Wellness | 2 jobs Fitness / gym | 2 jobs Gear | 1 jobs Transparency | 1 jobs Medical leave | 1 jobs

Salary Composition for Deep Learning Engineers

The salary for a Deep Learning Engineer typically comprises a base salary, performance bonuses, and additional remuneration such as stock options or equity, especially in tech companies. The composition can vary significantly based on the region, industry, and company size. In the United States, for instance, tech hubs like Silicon Valley often offer higher base salaries and substantial equity packages compared to other regions. In Europe, the base salary might be lower, but companies often compensate with generous benefits and bonuses. In smaller startups, the base salary might be modest, but the potential for equity growth can be significant. Conversely, larger corporations might offer a more stable salary with structured bonuses and less equity.

Steps to Increase Salary

To increase your salary from a Deep Learning Engineer position, consider the following strategies:

• Specialize in Niche Areas: Developing expertise in niche areas of deep learning, such as reinforcement learning or natural language processing, can make you more valuable.
• Pursue Leadership Roles: Transitioning into roles like a team lead or a managerial position can significantly boost your salary.
• Continuous Learning: Stay updated with the latest advancements in AI and machine learning. Attending workshops, conferences, and pursuing further education can enhance your skills.
• Network and Collaborate: Building a strong professional network can open up opportunities for higher-paying positions.
• Negotiate Offers: When switching jobs, negotiate your salary based on your experience and the value you bring to the company.

Educational Requirements

Most Deep Learning Engineer positions require at least a bachelor's degree in computer science, data science, mathematics, or a related field. However, a master's degree or Ph.D. is often preferred, especially for roles that involve research and development. These advanced degrees provide a deeper understanding of machine learning algorithms, data structures, and statistical methods, which are crucial for developing sophisticated AI models.

Helpful Certifications

While not always mandatory, certain certifications can enhance your credentials and demonstrate your expertise in deep learning:

• TensorFlow Developer Certificate: Validates your ability to build and train neural network models using TensorFlow.
• AWS Certified Machine Learning – Specialty: Demonstrates your skills in designing, implementing, and maintaining machine learning solutions on AWS.
• Microsoft Certified: Azure AI Engineer Associate: Shows proficiency in using Azure AI services to build and deploy AI solutions.
• Google Professional Machine Learning Engineer: Certifies your ability to design, build, and productionize machine learning models.

Required Experience

Typically, employers look for candidates with at least 2-5 years of experience in machine learning or data science roles. Experience with deep learning frameworks like TensorFlow, PyTorch, or Keras is crucial. Additionally, hands-on experience in deploying machine learning models in production environments and working with large datasets is highly valued.

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