Landing a Job in Artificial Intelligence
| Will Capella
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Modern artificial intelligence (AI) may not look like the self-aware robots from science fiction, but the technology already permeates everyday life. AI accounts for cutting-edge technology in self-driving cars, digital assistants, and personalized recommendations on the web.
AI forms a unique discipline that combines with almost any other field and industry. The AI field remains cutting edge. AI specialists lead the way in new and emerging technology, which can pose intriguing challenges.
If those qualities appeal to you, consider pursuing a job in artificial intelligence. Establishing a foundation for a career in AI can take a lot of time and a commitment to lifelong learning. However, AI provides a host of exciting and lucrative opportunities.
What is Artificial Intelligence?
Modern artificial intelligence extends beyond the basics of machine learning. AI uses multiple sets of algorithms to create an artificial neural network through a specialized process called "deep learning".
According to Builtin, the major difference between machine learning and deep learning comes from human input. Traditional machine learning requires humans to organize and process data before feeding it into a series of algorithms. Deep learning incorporates that step and can process a huge amount of raw data with little to no human intervention. Despite that distinction, the term may be used interchangeably.
Modern AI responds to the overwhelming surge of data: Statista reports that humanity recorded a total of 64.2 zettabytes of data by 2020, and the world barrels towards creation of 181 zettabytes by 2025. For scale, a single zettabyte equates to one trillion gigabytes.
Where Can I Work in AI?
AI specialists can find positions such as machine learning engineers, information research scientists, and data analysts.
Developments in AI impact nearly every industry and organization. The government uses AI in data analysis to detect and counteract threats. Nonprofits adapt AI technology to spread awareness of ongoing water crises and other causes through chatbots. From household products like Amazon's Alexa to developments in medical diagnosis, AI reaches across every field.
Developments in AI impact nearly every industry and organization.
Many AI positions concentrate in cities that already feature a thriving tech industry. The cities with the largest job concentrations include Seattle, New York, and San Francisco. While remote positions exist, applicants may find more open positions in urban areas.
The organizations below curate development in AI and reveal how the tech applies to multiple disciplines.
The tech and social media giant uses AI in its Oculus virtual reality products and software. With an emphasis on computer vision, the AI in Oculus headsets recognizes and tracks hand movement through cameras to interact with the virtual world.
The online retailer develops natural language processing and speech analysis software for its Alexa digital assistant products. As a conversational AI system, Alexa learns new speech patterns to adjust to each customer.
The renowned researchers of space began developing AI systems to help process and organize the overwhelming amount of data in the universe. NASA hopes to create a system to determine habitable planets based on the chemical makeup of wavelengths of light .
Tesla's self-driving software relies heavily on AI development and machine learning. The AI processes information such as traffic laws, street markings, and external cameras to perceive the road and respond to real-time events.
Ezra combines AI with MRI technology to help detect cancer earlier and more accurately. The AI provides a more accurate 3-D rendering of internal organs and segments image scans to help radiologists identify abnormalities.
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Artificial Intelligence Career and Salary Outlook
The high demand for qualified AI specialists rewards experts in the field with lucrative pay rates and considerable freedom. Employees seeking variety can potentially jump from opportunity to opportunity with the right experience.
The Bureau of Labor Statistics projects a 15% increase in computer and information research jobs from 2019-29, which includes positions in AI. The median annual salary for this role reached $126,830 as of May 2020, with the potential to earn more in managerial roles.
Likewise, the PayScale reports for machine learning engineers show the profitability of a job in AI. As of July 2021, the average annual salary for these engineers reached $112,930. The annual average salary for entry-level machine learning engineers sits at $93,340 while the reports from late-career employees (20+ years experience) show an average of $162,000.
Different specializations, especially when working on AI for different industries, can impact the expected salary. While earning a degree helps applicants find a job in the field, experience may matter more in the long run.
How to Break Into the Field of AI
While plenty of AI careers exist, breaking into the field might take some time. Some experts recommend pursuing a postgraduate degree for structured learning, networking, and recognition in the field. However, the value of a strong portfolio continues to increase. Prior experience in programming or tech development can turn an applicant into a new hire.
All AI-focused positions require advanced levels of tech knowledge and programming skills. That said, AI professionals can benefit greatly from a background in other fields like psychology. In all cases, experts recommend following the flow of cutting-edge technology to stay on top of developments in the industry.
Many employers prefer applicants with a master's degree in computer science or higher. Traditional education can provide a strong framework for a career in AI. Earning a postgraduate degree can also help you apply a fresh, well-rounded perspective to AI. A strong portfolio and previous experience in programming may suffice in lieu of a postgraduate degree. However, many students find networking easier within the context of graduate-level education.
Along with traditional education, prospective employees in the field can take professional development courses such as bootcamps. Bootcamps can expand a worker's programming skills, and some organizations like Springboard offer AI programs.
The curriculum for AI education varies depending on the school and type of degree. A general computer science degree provides a holistic understanding of technology, but offers fewer AI classes. In comparison, a specialized degree in AI can offer an in-depth study of the field, with a reduced focus on other areas of computer science.
However, some topics tend to remain consistent across multiple curriculums. The examples below highlight the type of AI courses you can expect while researching educational programs.
- Principles and Techniques of AI: This course demystifies the field of AI by introducing the basic theories and design of modern AI systems. Students engage with machine and deep learning through examples of AI systems encountered in everyday life. Participants also explore basic algorithm design and testing.
- Computer Vision and Image Processing: As the title implies, this course covers computer vision in AI. Students learn the basics of image processing and recognition, including applications for neural networks. Participants use industry-standard software while exploring concepts like image classification, detection, and segmentation.
- Natural Language Processing in Deep Learning: Natural language processing explores how neural systems understand human language. This course combines linguistics and cutting-edge computer science to develop systems that can process and evolve with human language.
- Algorithms and Implementation in Deep Learning Systems: This class covers advanced programming and software engineering in AI. Students learn full-stack programming in relation to deep learning and develop models from scratch. At the end of the course, participants create a deep learning library, including working models of sequential tasks and image classification.
- Machine Learning Ethics and Society: Students in this course learn to navigate difficult moral choices in the field and employ ethical techniques for AI development. Participants examine how AI can exacerbate current issues in society, such as discrimination or violations of privacy. This course prepares enrollees to avoid these issues and handle new ones as the ethical challenges of AI evolve with the industry.
Since AI integrates with multiple disciplines inside and outside of computer science, the required job skills vary. Some positions in AI, like a Sensor Algorithm Engineer from Google, emphasize hardware and data collection through electronic sensors. Other jobs focus entirely on software engineering and double down on required programming languages.
Throughout all positions, many soft skills remain universal. Most jobs in AI require collaboration with team members or other departments to develop new technology. Good leadership skills can also make or break promotion opportunities down the line.
While the required skills change depending on the company and industry, you can jumpstart your path to an AI career by learning the programs listed below.
- General Purpose Languages: Python and C/C++
- Database Management: Apache Cassandra, Couchbase, DynamoDB
- Data Analysis and Statistics: MATLAB, R,pandas
- AI Platforms: Microsoft Azure AI, Google Cloud AI, IBM Watson
- Data Acquisition Systems: Physical sensors (acoustic, visual, magnetometer, gyroscopes, etc.) and wireless sensors (Wifi, UWB, etc.)
While education provides a solid foundation for job searching, previous experience can distinguish desirable job candidates from the rest. Toward this end, students can benefit from joining AI-focused communities. Professional organizations and other groups in the field offer amazing networking and experiential opportunities.
Students can begin building their portfolios early through school projects and internship opportunities. If possible, they should try to work on published projects or publish personal projects themselves through platforms like GitHub. Bootcamps can provide experience and education at the same time, including the chance to develop a professional portfolio.
For entry-level positions, some employers hire graduates with as little as one year of hands-on experience working with machine learning and software development. For other roles, employers might prefer alternate experience and education such as a background in grammar or communications.
Locating a Job in AI
AI jobs appear on most major online job boards including Indeed, LinkedIn, Glassdoor, ZipRecruiter, and Google Job Search. A small handful of niche sites focus on AI-related positions such as AI Jobs Board, but larger job boards may include a wider selection. Some job search sites like Dice also feature hiring events and conferences such as the Diversity in Tech Event.
Prominent companies such as Google, Microsoft, and Facebook typically need to fill a lot of open positions in AI. Applicants may also look into other companies leading the charge in AI development: Ascent, H20.ai, and Grammarly. Grammarly focuses on the linguistics side of AI, while Ascent needs specialists to help create the next generation of robots.
Most industries have integrated some form of AI into their operations. Applicants should look outside the computer science discipline for AI-related jobs too.
- With over 72,000 jobs listed as of July 2021, Dice is one of the biggest tech job boards online. The site features multiple remote work filters and other ways to narrow down a search.
- AI Jobs Board
- One of the few niche sites dedicated to jobs in AI, AI Jobs Board features 8.5 million views each year and more than 120,000 subscribers. Candidates can search for positions or post their resumes to the site so employers can find them.
- This no-frills job site provides an easy-to-navigate interface with updated job listings. Each listing links directly to the employer's website so you can avoid applying to expired jobs.
Frequently Asked Questions
Artificial intelligence divides into four levels of autonomy: reactive machines (lowest), limited memory, theory of mind, and self-awareness (highest). The big research topics in AI revolve around computer vision, speech analysis, and natural language processing.
There are three main goals: to create strong AI, create applied AI, and/or advance cognitive simulation. Strong AI applies to self-aware machines. Applied AI uses technology to further existing fields such as medicine. Cognitive simulation involves learning more about the human brain through AI.
While you may not need to specialize in AI for cybersecurity jobs, you will likely encounter it as a part of the job. AI systems can help security systems learn from past attacks, but hackers also use AI to circumvent new defenses.
The CIA offers a host of positions for computer science graduates such as applications developer, data analysis officer, digital targeter, and technical project engineer. The CIA also provides computer science scholarship opportunities for undergraduate and graduate students.
So far, AI is neither good nor bad. Technology has not reached a point of developing self-awareness, so the effects of AI depend on how people use it. AI can speed up medical diagnostics and save lives; it can also be used to steal sensitive information.
Feature Image: Westend61 / Getty Images
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