AI/ML Engineer at Linkedin

Salary💰

The average salary for the AI/ML Engineer at LinkedIn is below.

However, the salary figures which are mentioned above are subject to change.

Let's discuss some benefits and perks the company provides for the AI/ML Engineer at LinkedIn.

Benefits and Perks💸

LinkedIn offers a range of benefits and perks to its employees.

• LinkedIn allows its employees to take paid time off to rest, refuel and recharge.
• It provides life insurance to its employees.
• It also provides its employees with a wide range of premium accounts, LinkedIn learning subscriptions, and discounts on Microsoft products.
• It has a week-long shutdown at the end of the year.
• It provides health insurance for its employees as well as their families.
• It has a wellness program that allows employees to use their onsite gym, fitness classes, and events.

Roadmap to Become AI/ML Engineer at LinkedIn.🛣️

The roadmap to becoming an AI/ML Engineer at LinkedIn is

Getting a Degree

Starting with the basics, you must complete an undergraduate course first.

To become an AI engineer, you must first have a degree in one of the following fields: computer science, mathematics, information technology, statistics, finance, and economics. Your grades signify your genuineness; thus, a high GPA would benefit you.

Building Technical Competencies

After finishing an undergraduate study in one of the above-mentioned streams, the next stage is to develop technical competencies, which include:    

Programming Languages

You must be extremely knowledgeable in programming languages such as Python, R, Java, C++, and others. It is essential to have a solid understanding of object-oriented programming.

Statistical Knowledge

Regarding stats, you should be familiar with vectors and network augmentation. Understanding subordinates, integrals, Gaussian distributions, means, and standard deviations would be beneficial.

Applied Math in Machine Learning

To be a successful AI engineer, you must have a good understanding of mathematical concepts. Artificial intelligence will need subject knowledge; learning Gradient Descent, Lagrange, Quadratic Programming, Partial Differential Condition, and others would benefit you.

It may seem intimidating from the outside if you've been away from math for a while. Be aware that Machine Learning and Artificial Intelligence are far more math-intensive than things like front-end development.

Natural Language Processing

Natural Language Processing and computer science combine to form one of the most important areas of Machine Learning and Artificial Intelligence. The likelihood of you dealing with it is quite high.

It is necessary to have extensive control over libraries like Gensim and NLTK and algorithms such as word2vec, Sentimental Analysis, and Summarization.

Deep Learning and Neural Networks

Occasionally, you may want Machine Learning for tasks that are excessively difficult for individuals to write legitimately. This is when neural networks come into play.

Neural systems are modeled after the human cerebrum, which can interpret numerical instances based on tactile input.

In artificial intelligence, single-layer neural networks are often called Deep Learning neural systems.

Deep neural networks have been the most precise technique for approaching complicated challenges. These are analogous to Translation, Speech Recognition, and Image Classification, all of which play important roles in AI.

SPARK

Spark is a data analytics engine mostly used for large-scale data processing. It has high-level APIs in Scala, Java, Python, and R and improved software that allows generic calculation diagrams for data analysis.

It also supports many higher-level devices, for example, Spark SQL for SQL and DataFrames, MLlib for AI, GraphX for chart Preparation, and Structured Streaming for stream handling.

Big Data Technologies

Big Data Technology is software technology designed to analyze, process, and extract information from massive datasets.

Traditional data processing software would never be able to handle this. We require Big Data Processing Technologies to analyze this massive amount of real-time information and make conclusions and predictions to reduce future risks.

Hadoop, Pesto, and MongoDB are examples of some Big Data Technologies

Below we list some of the most asked topics.

• Data Structure
• Algorithm
• Machine Learning
• Deep Learning
• Natural Language Processing
• Python

Necessary Business Skills to Become Artificial Intelligence Engineer

To create a career in AI, you must have some soft skills in addition to the skills listed above. These soft skills are as follows:

Innovative reasoning

It is critical for an AI engineer to be able to think creatively. Developing AI is about thinking outside the box and being creative, which requires innovative reasoning.

Problem Solving Skills

AI is a means for people to tackle complicated issues. Problem-solving abilities are essential for developing such skills.

Ability to work in a team

The importance of teamwork cannot be overstated. Team players are typically preferred in the technological field.

Information about the industry: To work in any business, it is necessary to have a thorough understanding of that industry and others that are linked to it. The same is true for AI engineers.

Interview Preparation 💻

During the selection process, the interview is crucial. It is also the final step that determines whether or not a candidate should be chosen.

As a result, you must prepare well to ace the interview. You should be prepared to answer various interview questions, ranging from situational and scenario-based queries to technical inquiries. Also, go through your resumes.

To Know more about Machine Learning and Artificial Intelligence, go through this video.

Hopefully, this post has given you enough information regarding the AI/ML Engineer position at LinkedIn.

Finally, I'd like to encourage you to be consistent, confident, and believe in yourself.

You may also use Coding Ninjas' interview preparation material.

• Interview Ready
• Interview Preparation Guide
• Test-Series
• Contests
• Machine Learning
• Data Science
• Top Problems
• Aptitude
• Last Minute Preparation Guide

 

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Frequently Asked Questions

What are the Qualifications to be an AI/ML Engineer at LinkedIn?

The AI/ML Engineer must have at least six years of relevant professional experience, an MS or Ph.D. in Computer Science or a related technical subject, and be fluent in English. Knowledge of machine learning, optimization methods, and/or deep-learning approaches is preferred.

What exactly does an AI/ML engineer do?

Machine Learning Engineers are highly trained programmers that create artificial intelligence (AI) systems by researching, developing, and generating algorithms that can learn and predict.

What Jobs Are Similar to the Role of a Machine Learning Engineer?

Many data professionals undertake comparable duties to those of a Machine Learning Engineer within the larger field of data science. Here are a few roles that might be part of a Machine Learning professional's career path: Data Scientist, Data Analyst, Software Engineer, and Data Engineer 

Who does a Machine Learning Engineer work with?

An ML Engineer would most likely work as part of a bigger data science team, depending on the firm's size. Data Scientists, Data Analysts, Data Engineers, Data Architects, and Database Administrators could be on that team.

Name a few popular Machine Learning Tools.

Some popular Machine learning tools that ML engineers find beneficial are: TensorFlow, Spark, Hadoop, R Programming, Apache Kafka, and MATLAB are some of the technologies used.

Conclusion

Congratulations, Now you know how to be AI/ML Engineer at LinkedIn, go practice. We have discussed the responsibilities, required Skills, Salary, etc., for the AI/ML Engineer at LinkedIn.

Refer to our guided paths on Coding Ninjas Studio to learn more about DSA, Competitive Programming, JavaScript, System Design, etc. Enroll in our courses and refer to the mock test and problems available; look at the Top 150 Interview Puzzles, interview experiences, and interview bundle for placement preparations.

Happy Learning!