Career Tracks

Machine Learning & AI

101 What Is Machine Learning & AI?

AI is the field of technology that makes computer systems capable of mimicking human problem solving. Machine Learning is a subset of AI. Expect to start out in a data-driven role and move into more specialized AI roles. The average salary for a Machine Learning Engineer is $159,000.

Jump to a list of all Machine Learning & AI Bootcamps

What is AI and Machine Learning?

The umbrella term, Artificial Intelligence (AI), has existed since the 1950’s, but has accelerated rapidly in the last 10 years. Machine Learning (ML) is a subcomponent of AI that uses specific statistical algorithms to process massive amounts of data in order to produce insights, predictions, and unique outputs.

As Evan Shy, the CEO of Coding Temple, describes: “The World Economic Forum predicts that tech advancements, from automation, artificial intelligence, to robotics, will displace 85 million jobs by 2025. However, this same technology will also create 97 million new jobs in areas like data analysis, software development, and cybersecurity. Ultimately it’ll depend on how you prepare for these inevitable changes.”

Machine Learning + AI Jobs

Traditional tech roles like Software Engineers and Data Scientists can incorporate AI and Machine Learning skills into their current jobs. For example, a Data Analyst might use the built-in AI feature in Tableau called "Explain Data" to uncover insights. Or an experienced QA Tester may use GitHub Copilot to write automated test scripts. However, in 2023, companies are now hiring for AI-specific roles like Prompt Engineer and AI Integration Specialist. Expect a lot of variability between job listings until these roles become more defined.

Here are some common ML/AI jobs: 

  • Data Scientist
  • Data Engineer
  • Prompt Engineer
  • AI Engineer
  • Software Engineer 
  • Product Manager
  • AI Ethics Officer
  • AI Data Curator
  • AI Trainer
  • AI Integration Specialist

Machine Learning/AI Engineer Job Description

Expect a job description for a Machine Learning Engineer or AI Engineer to ask for knowledge of Python, Spark, , and. You may also see generative AI tools like ChatGPT or OpenAI. Codesmith’s Director of Machine Learning, Weylin Wagnon, says that “You need to be able to work with large amounts of data, be a smart programmer, understand neural networks, and have machine learning skills if you want to build the next generation of tech products. And if you don’t, you’ll be left behind over the next 10 to 15 years. It’s hard to observe the future of jobs and not be scared of how machine learning is taking over; I think the best way to stem that tide is to get into the field yourself.”

What Does a Machine Learning or AI Engineer Do?

Wagnon says that “In general, machine learning is equal parts math, statistics, computer science, and voodoo.” 

Varun Kumar, an AI Engineer who graduated from Flatiron School, says that his job is “part data wrangling, part coding, and part researching new techniques and software that has been developed in dealing with large language models and processing natural language.” Varun breaks it down even further into 6 categories of on-the-job requirements:

  • Research: Stay updated with the latest advancements in the field. This could involve reading research papers, attending seminars or webinars, and participating in online forums and communities. This is crucial as the field of AI and machine learning is evolving rapidly.
  • Data Preparation: Work on preparing and pre-processing the data for training language models. This involves collecting data, cleaning it, and converting it into a format that can be used for machine learning.
  • Model Development and Training: Design and implement machine learning models. This includes choosing the right algorithms, tuning parameters, and training the model on the prepared data. This process often requires running experiments and making iterative improvements based on the results. Many times, I am building on pre-trained models with either fine tuning, or instruction via prompts.
  • Model Evaluation: Evaluate the performance of the models using appropriate metrics. This often involves testing the model on a held-out validation set and analyzing the results.
  • Collaboration: Work closely with other teams, such as product development, to integrate the AI models into products or services. This could involve optimizing the model for deployment, working on the user interface, or addressing user feedback.
  • Documentation and Presentation: Document the work for future reference and present findings to stakeholders or to the technical team. This might involve writing technical reports, creating presentations, or showing working code.

ML + AI Engineer Salaries

The average Machine Learning Engineer salary is $158,676 per year with up to $177,583 in cities like San Francisco. Newly-created roles like Prompt Engineer can earn up to $335,000 per year! 

ML/AI Skills

According to Carianne Burnley, a Career Coach at Springboard, “The most widely used programming language in AI is Python, and the libraries and frameworks associated with it. Knowing other languages like Java and/or C++ can be helpful as well.”

Most Important AI Technical Skills + Languages:

  • Python
  • Databases
  • Big data tools like Spark
  • Cloud platforms like AWS or Azure
  • Data visualization tools like Tableau, PowerBI, or R
  • Mathematics like linear algebra, data interpretation, and deep learning.

Soft Skills for AI and Machine Learning

Burley says that if you are interested in becoming an AI Engineer, you will likely have these three soft skills: 

  1. Curiosity and the hunger to continually learn more 
  2. Strong communication skills 
  3. Motivation

How to get into Machine Learning/AI

Machine learning & AI bootcamps last between 12-24 weeks and cost anywhere from free to $30,000.

Realistically, a complete beginner in tech is not going to become an AI Engineer in 12 weeks. If you don't have a technical background, then start with a Software Engineering or Data Analytics bootcamp. Once you graduate, find an entry-level job working with data, and continue to learn! You can add to your skillset with one of these 17 AI Intro courses


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