Machine Learning Jobs in the United States

Machine Learning Jobs Overview

Machine learning roles are on the rise, and we bet you want to be part of this exciting world. The World Economic Forum predicts that we’ll see a 40% growth in machine learning jobs over the next five years. So, buckle up to find out about all the roles this field includes.

Machine learning (ML) is a subfield of artificial intelligence (AI) that aims to train algorithms with data to perform various tasks. It empowers computers to recognize patterns and make accurate predictions and decisions. To do so, it requires the collaboration of different roles. Here are some of them:

• Machine learning engineer: designs, develops, and implements algorithms and models to create intelligent systems that can learn from and improve data.
• Data scientist: analyzes and interprets complex data sets to extract valuable insights. They use machine learning techniques to build predictive models and drive informed decision-making.
• Natural language processing (NLP) engineer: specializes in developing algorithms and models that enable computers to understand, interpret and generate human language. Their work is used, for example, in chatbot creation and language translation.
• Computer vision engineer: works on algorithms that allow computers to interpret and process visual information from images and videos. That enables applications like facial recognition and autonomous vehicles.
• Deep learning researcher: focuses on exploring and advancing complex neural network architectures. They deal, for instance, with image recognition, natural language processing, and medical diagnoses.
• AI ethicist: ensures that the machine learning systems and algorithms are developed and deployed in a responsible and fair manner. They address potential biases, privacy concerns and other ethical implications.
• Quantum machine learning specialist: uses quantum computing principles to develop approaches that process and analyze data faster and more efficiently than classical computers.
• Machine learning cloud architect: focuses on deploying and managing machine learning models in cloud environments. They ensure scalability, efficiency, and seamless integration with other services.
• Computational linguist: designs approaches to process and analyze human language data. They play a crucial role in NLP applications, search engines and language technologies.
• Financial machine learning analyst: uses ML to examine market trends, predict stock prices and enhance investment strategies. They also analyze extensive financial datasets.

Machine learning jobs are usually full-time office or remote roles.

Salaries for Machine Learning Jobs

The salary you can make in jobs in machine learning depends first on the specific position you occupy. Check out some common roles and their annual range of compensation:

• data scientist: $79,183 to $133,041
• big data architect: $81,628 to $148,320
• data engineer: $83,285 to $138,520
• data analyst: $45,872 to $103,124
• automation engineer: $75,704 to $116,495

The industries in which these machine learning jobs sit also influence how much you can make. Below are the top-paying industries and their median wages per year:

• clothing and clothing accessories retailers: $164,030
• computer and peripheral equipment manufacturing: $158,350
• semiconductor and other electronic component manufacturing: $148,270
• automotive parts, accessories, and tire retailers: $143,290
• securities, commodity contracts, and other financial investments and related activities: $133,730

Finally, location matters too. Here are the top-paying states for this field:

• California: $147,390 per year
• Washington: $140,780 per year
• Virginia: $133,990 per year
• Delaware: $133,320 per year
• New Jersey: $129,980 per year

See for yourself how much you could make in your role and location with Monster’s Salary Calculator. Our free tool also shows salaries for similar positions as well as potential career advancements to consider.

How to Find the Best Machine Learning Jobs

Struggling to navigate the job market and set apart the right jobs in machine learning for you? Here is a job-hunting strategy to help you:

Identify Your Strengths and Interests

Start by recognizing your skills, expertise, and areas of interest within machine learning. For instance, are you interested in NLP, deep learning, computer vision or something else? Narrowing down your search will make it more effective.

Assess Job Listings

Look for roles that match your skills and goals overall. Don’t hesitate to apply if you don’t meet 100% of the requirements. Job descriptions often describe unicorn candidates. Read through the job listings and identify that you meet the mandatory requirements.

Screen Companies

Always research companies before applying to their machine learning jobs. Investigate what their focus, culture and values are. To understand their work environment, also read what previous employees say about them.

How to Apply to Machine Learning Jobs

Ready to surf the AI wave and apply to machine learning jobs? Make your resume and cover letter irresistible to recruiters by following these tips:

Update Your Resume for Jobs in Machine Learning

The main rule you must live by when writing your resume is tailoring. Make sure to adapt your machine learning resume to each and every role you apply for. This is crucial to beat the applicant tracking systems (ATS) most tech companies use to initially filter candidates.

To do so, you must carefully read the job listing, find the keywords that best describe the position and include them in your resume. Some common examples of highly sought-after skills in ML are:

• Python programming
• deep learning
• model evaluation
• statistical analysis
• natural language processing (NLP)
• data manipulation

You must also highlight your past contributions with facts, AKA KPIs. Here are some examples of KPIs used in machine learning professions:

• model accuracy
• training time
• precision and recall
• inference speed

Need more guidance? Have Monster’s Resume Writing Services assist you. One of our expert resume writers will work with you to highlight your skills and experience and optimize your profile for your target role.

Cover Letter Tips

A cover letter is the best tool to highlight your accomplishments and connect with recruiters on a human level regarding your motivations and career goals. It doesn’t need to be long and should be succinct even though you’re including extra details that didn’t fit in your resume.

Here’s what to write:

• Introduction. Greet the recruiter by name or with a simple “Dear Hiring Manager” if necessary. State which role you’re applying for and mention your industry background.
• Showcase your fit. Highlight your relevant qualifications, experience and skills and mention how they align with the job requirements.
• Show enthusiasm. Express genuine enthusiasm for the role and the company. Explain why you want to work for them and demonstrate that you share the same values.
• Close strong. Sum up in a few sentences why you’re the candidate they need. Then, invite them to review your resume.

When your resume and cover letter are good to go, upload them to your free Monster candidate account. As a member, applying to future jobs is fast and simple. Plus, we’ll keep you posted on all the latest job listings matching your profile.

How to Follow Up with an Employer

Haven’t heard back from one or more jobs in machine learning that you applied for? Take the matter into your own hands and follow up with the employers. Here’s how:

• Get the timing right. Wait at least a week after submitting your application. This will give recruiters enough time to screen all the candidates.
• Contact the right person. Reach out to the recruiter mentioned in the job posting or research who they are on the company’s website. You can also simply call and ask for the right contact.
• Choose the best medium. At this point in the recruiting process, a professional and short follow-up email is the perfect medium.
• Nail your subject line. Pick a concise and relevant subject line such as “Follow-up on XY Job Application – [Your Name]”.
• Get straight to the point. Greet the recruiter by name, then state which role you applied for. Next, express your continued interest and enthusiasm for the role.
• Highlight your fit. Briefly mention your broad background and a couple of skills that make you the perfect candidate. Keep it to one or two sentences.
• Request an update. Politely ask about the status of your application and thank the recruiter for their time.

Interviewing Tips for Machine Learning Jobs

To lock in a role in machine learning you’ll have to pass one or more interview rounds. To pass those it’s essential you first understand what each type of interview entails. Then, regardless of your experience, you should practice answering the most common interview questions such as:

• Tell me about yourself.
• What motivated you to apply for this position?
• What do you know about our company and products and services?
• What are your strengths and how do they relate to this job?

Next, review the technical knowledge of your specific role. They might ask you to explain the following:

• Concepts or algorithms, for example, “What’s your approach to using KNN for image processing?”“, or “How can we use a dataset without the target variable in supervised learning algorithms?”.
• Real-world applications for systems, for example, “Can you give an example of a real-world problem that could be solved using X system?”
• Writing code, for example, “Write code to implement XY task using a programming language of your choice.”

You’ll almost certainly also face situational or behavioral questions. These are designed to understand how you have or would behave in certain situations. Use the STAR method to nail your answers. And finally, always make sure to send a professional thank-you note within 24 hours to help recruiters solidify a good impression of you.

What to Do When You Get an Offer

You got an offer, well done! What should you do now? Here is a step-by-step guide:

1. Say thank you. Promptly replying shows professionalism and allows you to express appreciation for the offer.
2. Request time. Agree with the employer on a defined timeframe to review the offer. For senior roles you can generally ask for more time than for entry-level machine learning jobs.
3. Review the offer. Carefully review all the aspects of the offer. Remember that work location, schedule, career development opportunities and company culture are just as important as pay and benefits.
4. Ask questions. Don’t hesitate to ask questions to clarify any detail of the offer that is unclear.
5. Negotiate if necessary. If you’re not fully satisfied with certain aspects of the offer, build your case to negotiate better terms.

Machine Learning Career Paths

Machine learning jobs can take your career in various directions depending on your interests, skills, and goals. Here are four of the many senior roles you could ascend to:

• Director of machine learning: in this role, you’d manage the entire ML division of an organization. You’d set strategic goals, lead teams, and drive innovation in AI projects.
• AI senior consultant: as an AI consultant, you’d guide businesses in developing AI strategies. You’d help them implement machine learning solutions and navigate ethical considerations.
• Professor of machine learning: put your machine learning experience to work, teaching the next generation of AI professionals. You’d also lead research projects and publish papers about ML field.
• Principal machine learning scientist: Another great option is becoming a lead researcher. You’d develop new algorithms, contribute to advancements in the field and collaborate on high-impact projects.

Similar Occupations

Looking for similar opportunities? Check out these popular options:

• AI jobs
• computer jobs
• computer science jobs
• coding jobs
• data scientist jobs