8 Machine Learning Jobs That Are In-Demand in 2024

Want to boost your career prospects? Learn machine learning. It's a powerful technology that can help you be a valuable asset to any team. With machine learning skills, you can collect, clean, analyze, and model data to uncover valuable insights and create impactful predictions.

In this article, we'll explore some of the top machine learning jobs available, discuss how to prepare for them, and help you determine if a career working with machine learning is right for you.

Why choose a career in machine learning?

Machine learning careers are in high demand and poised for significant growth, making it an attractive field for aspiring data professionals. As machine learning becomes increasingly integrated into business decision-making, the opportunities for those with relevant skills are practically endless.

Recent statistics paint a promising picture. The global Machine Learning (ML) market size was valued at \$19.20 billion in 2022. The market is expected to grow from \$26.03 billion in 2023 to \$225.91 billion by 2030, at a CAGR of 36.2%.* Machine learning engineer jobs in the US are also projected to see a 31% growth from 2019 to 2029, much faster than average.*

Pursuing a machine learning career offers several key benefits:

• Job security: Sustained demand across industries ensures ample long-term opportunities.
• Growth potential: Professionals can expect substantial career development and advancement.
• Strategic impact: Machine learning roles allow you to shape business decisions through data-driven insights.

Machine learning professionals play a vital role in transforming raw data into predictive models and actionable intelligence. By developing expertise in this powerful technology, you can position yourself for a rewarding career at the forefront of data-driven innovation.

Top 8 Machine Learning Jobs

Thinking about launching a career in the fast-growing field of machine learning? You've picked an excellent time! With businesses increasingly leveraging ML to drive innovation and efficiency, the demand for talented professionals is rapidly increasing. From tech giants to scrappy startups, companies are on the hunt for individuals with the right mix of technical skills and problem-solving prowess.

While AI is certainly transforming the machine learning landscape, it's not eliminating the need for human expertise. In many ways, the rise of AI is actually fueling the creation of exciting new roles for those who can wield it strategically.*

So, what are the most sought-after positions in the world of machine learning? We've crunched the numbers and analyzed hiring trends to identify the top 8 jobs you should know about:

1. Machine Learning Engineer
2. Data Scientist
3. Software Engineer
4. AI Research Scientist
5. Natural Language Processing Engineer
6. Business Intelligence Developer
7. AI Product Manager
8. AI Engineer

In the coming sections, we'll shine a spotlight on each of these roles, exploring the day-to-day responsibilities, must-have skills, and typical career trajectories. Whether you're just starting out or looking to level up, you'll come away with a clearer sense of which path aligns best with your unique mix of interests and abilities. Let's jump in and explore!

1. Machine learning engineer

Machine Learning Engineers are the masterminds behind intelligent systems that learn from data. They live and breathe algorithms, using frameworks like TensorFlow and PyTorch to build sophisticated models that drive innovation.

On a typical day, they might be knee-deep in preprocessing data, designing neural networks, optimizing models, or collaborating with teams to bring ML to life in real-world applications.

Salary: \$112K - \$157K/yr (Glassdoor)

Responsibilities:

• Design and deploy ML models to solve complex problems
• Build deep learning models using cutting-edge frameworks
• Collaborate on end-to-end ML pipelines
• Optimize models for performance and scalability

Key Skills:

• Strong programming skills like Python
• Solid math foundation (linear algebra, calculus, statistics)
• Experience with ML frameworks (scikit-learn, PyTorch)
• Knowledge of ML ops best practices

Machine Learning Engineers are at the vanguard of the AI revolution, crafting the intelligent systems that are reshaping industries. As businesses increasingly harness the power of ML, the demand for these skilled professionals is skyrocketing.

2. Data scientist

Data Scientists are the detectives of the big data era, wielding the power of statistical analysis and machine learning to uncover game-changing insights hidden in vast datasets. They're the bridge between raw data and strategic decision-making.

Compared to other machine learning roles, Data Scientists often focus more heavily on the analytical side, leveraging their strong statistical foundations to drive research and uncover key patterns. They're the ones finding the needle of insight in the data haystack.

A day in the life of a Data Scientist might involve wrangling messy customer data, exploring variables to predict churn, building sophisticated prediction models, and translating complex findings into clear, actionable recommendations for stakeholders.

Salary: \$97K - \$167K/yr (Glassdoor)

Responsibilities:

• Wrangle, preprocess, and analyze complex datasets to uncover insights
• Train and validate machine learning models for prediction and inference
• Communicate data-driven findings and recommendations to drive business strategy
• Collaborate across teams to define data needs and guide analysis

Key Skills:

• Strong foundation in statistics, probability, and data analysis techniques
• Proficiency in languages like Python or R for data manipulation and modeling
• Knowledge of supervised and unsupervised machine learning algorithms
• Ability to clearly visualize and communicate complex data insights

In an increasingly data-driven world, Data Scientists play a pivotal role in helping organizations harness the full potential of their data assets. As companies seek to gain a competitive edge through data-informed strategies, the demand for skilled Data Scientists shows no signs of abating.

3. Software engineer

Software Engineers are the builders behind intelligent applications, seamlessly integrating cutting-edge ML models into user-friendly software products. They bridge the worlds of software development and artificial intelligence to create tools that harness the power of data.

Software Engineers differ from other ML roles by concentrating heavily on the entire software development process, from initial design to final deployment. They specialize in converting ML prototypes into production-ready applications that can make a real impact in the world.

On a typical day, a Software Engineer might be found preprocessing datasets, experimenting with model architectures, optimizing hyperparameters, and integrating trained models into software systems. It's all about striking the perfect balance between performance and usability.

Salary: \$92K - \$158K/yr (Glassdoor)

Responsibilities:

• Design and develop ML-powered software applications from start to finish
• Collaborate with cross-functional teams to understand product requirements
• Maintain and improve codebases for efficiency and reliability
• Integrate and optimize ML models for performance and scalability

Key Skills:

• Strong programming skills in languages like Python, Java, or C++
• Familiarity with ML libraries such as TensorFlow or PyTorch
• Solid understanding of software engineering principles and best practices
• Excellent problem-solving skills for debugging and optimization

As businesses increasingly seek to put machine learning into the hands of users, skilled Machine Learning Software Engineers are in high demand. By building the bridge between powerful ML models and intuitive applications, these professionals are driving the widespread adoption of artificial intelligence across industries.

4. AI research scientist

AI Research Scientists are the trailblazers of the machine learning world, constantly pushing the boundaries of what's possible with artificial intelligence. They're the masterminds behind the cutting-edge algorithms and approaches that are revolutionizing industries and shaping our future.

While other machine learning roles focus on applying existing techniques, AI Research Scientists are all about innovation. They dive deep into the theoretical underpinnings of ML, dreaming up novel solutions to the most complex challenges. Most positions require an advanced degree and a proven track record of groundbreaking research.

AI Research Scientists spend their days immersed in the latest deep reinforcement learning research, crafting experiments to test promising new architectures, and working with colleagues to transform their discoveries into publishable papers. The role requires a balance of innovation, technical precision, and an unwavering commitment to pushing the boundaries of the field.

Salary: \$147K - \$246K/yr (Glassdoor)

Responsibilities:

• Conduct groundbreaking research to push the frontiers of AI capabilities
• Develop novel ML algorithms, architectures, and techniques to solve complex problems
• Collaborate with top researchers to produce and publish innovative work
• Communicate complex research findings to both technical and non-technical audiences

Key Skills:

• Ph.D. or equivalent experience in Computer Science, Statistics, or related field
• Deep theoretical understanding of machine learning, statistics, and mathematics
• Exceptional research abilities and creative problem-solving skills
• Strong programming skills for implementing and testing novel algorithms

As artificial intelligence continues to transform our world, the groundbreaking work of AI Research Scientists becomes increasingly vital. By constantly expanding the boundaries of what machine learning can achieve, these pioneers are not only advancing the field but also unlocking new possibilities for how AI can benefit society.

5. Natural language processing engineer

Natural Language Processing (NLP) Engineers are the language whisperers of the AI world, teaching machines to understand and communicate with humans. They're the masterminds behind the sophisticated language models that power everything from chatbots and virtual assistants to sentiment analysis and content moderation tools.

What sets NLP Engineers apart is their deep expertise in linguistic concepts and techniques. They're not just building generic ML models, but tailoring algorithms to navigate the complex nuances of human language. A strong foundation in both programming and linguistics is a must for success in this role.

On a typical day, an NLP Engineer might be found annotating massive text datasets, fine-tuning state-of-the-art transformer models for specific use cases, or integrating cutting-edge language understanding capabilities into user-facing products. It's all about bridging the gap between the way humans communicate and what machines can comprehend.

Salary: \$89K - \$145K/yr (Glassdoor)

Responsibilities:

• Design and develop advanced NLP models and pipelines for various language tasks
• >Implement sophisticated NLP techniques like parsing, tagging, and information extraction
• Evaluate and optimize model performance using metrics like BLEU, ROUGE, and F1 score
• Deploy and integrate NLP models into products like chatbots, voice interfaces, and text analyzers

Key Skills:

• Deep expertise in natural language processing techniques, models, and algorithms
• Strong programming skills, particularly in Python and popular NLP libraries like NLTK and spaCy
• Experience with deep learning architectures and frameworks commonly used in NLP
• Solid understanding of linguistic concepts such as syntax, semantics, and pragmatics

As businesses increasingly look to harness the power of language data, the demand for skilled NLP Engineers is rapidly growing. By enabling machines to understand and engage with human language, these professionals are driving the development of AI systems that can communicate with us on our own terms.

6. Business intelligence developer

Machine Learning Business Intelligence (BI) Developers are the bridge between raw data and actionable insights, leveraging the power of ML to guide strategic decision-making. They're the wizards who transform complex data into clear, compelling visualizations that drive business value.

While other machine learning roles focus on building models, ML BI Developers specialize in putting those models to work. They're experts at harnessing ML-powered insights to create interactive dashboards and reports that illuminate key trends and opportunities. SQL mastery and data visualization chops are the superpowers in this role.

On a typical day, an ML BI Developer might be found wrangling vast datasets, designing eye-catching visualizations to track critical metrics, or presenting game-changing insights to C-suite executives. It's all about transforming data into strategic ammunition that can give businesses a competitive edge.

Salary: \$86K - \$135K/yr (Glassdoor)

Responsibilities:

• Design and develop innovative BI solutions and data pipelines powered by ML insights
• Analyze complex data using advanced SQL techniques to uncover trends and opportunities
• Create stunning interactive dashboards and reports to communicate key findings
• Collaborate with cross-functional stakeholders to understand and meet evolving business needs

Key Skills:

• Deep expertise in SQL, database management, and data warehousing techniques
• Mastery of BI and data visualization tools like Tableau, Power BI, or Looker
• Exceptional analytical and problem-solving skills to derive insights from complex data
• Strong communication abilities to convey technical insights to non-technical audiences

In an increasingly data-driven business world, ML BI Developers play a crucial role in harnessing the power of artificial intelligence to inform strategic decisions. By illuminating the insights hidden in raw data, these professionals help organizations navigate complex challenges and seize emerging opportunities.

7. AI product manager

AI Product Managers are the visionaries who shape the future of machine learning products, guiding them from initial concept to successful launch and beyond. They're the strategic masterminds who coordinate the efforts of engineering, design, and business teams to bring game-changing AI innovations to market.

While other machine learning roles focus on the technical details, AI Product Managers take a higher-level view. They're less concerned with the specifics of model architectures and more focused on how ML can be harnessed to drive user engagement and business growth. Strong leadership skills, domain expertise, and a knack for strategic thinking are the keys to success in this role.

An AI Product Manager's typical day involves specifying requirements for innovative ML features, optimizing resource allocation by prioritizing the development backlog, analyzing user data to pinpoint improvement opportunities, and presenting a persuasive product roadmap to senior management. The role focuses on guiding the team towards the most effective and lucrative machine learning applications.

Salary: $117K - $158K/yr (Glassdoor)

Responsibilities:

• Define the overarching vision, strategy, and roadmap for cutting-edge AI/ML products
• Coordinate the end-to-end development of ML-powered features from ideation to launch
• Analyze product performance metrics and user feedback data to identify areas for optimization
• Communicate product plans, progress, and results to cross-functional stakeholders and leadership

Key Skills:

• Proven track record of successful product management for AI/ML-driven products
• Solid understanding of machine learning concepts, techniques, and applications
• Exceptional leadership, communication, and organizational abilities to drive complex initiatives
• Strategic mindset and strong analytical skills to make data-informed product decisions

As businesses increasingly look to harness the transformative potential of artificial intelligence, the role of the AI Product Manager has become more critical than ever. By charting the strategic course for game-changing ML products, these visionary leaders are shaping the future of how we live, work, and interact with technology.

8. AI engineer

AI Engineers are the architects who weave artificial intelligence into the fabric of our digital world, bringing the power of machine learning to bear on real-world challenges. They're the masters of integration, working tirelessly to embed cutting-edge AI capabilities into the products and applications we use every day.

What sets AI Engineers apart is their end-to-end understanding of the AI solution lifecycle. They're not just building isolated models in a lab, but collaborating closely with cross-functional teams to align technical solutions with business objectives. From data wrangling to model deployment and beyond, they oversee the full journey from raw data to real-world impact.

On a typical day, an AI Engineer might be found preprocessing massive datasets to fuel machine learning models, optimizing algorithms for peak performance in production environments, or designing elegant APIs to integrate AI capabilities into existing software architectures. It's all about bridging the gap between the boundless potential of artificial intelligence and the tangible needs of the business world.

Salary: \$96K - \$166K/yr (Glassdoor)

Responsibilities:

• Design and develop end-to-end AI solutions that align with business requirements
• Integrate advanced AI capabilities like computer vision, NLP, and predictive analytics into products
• Optimize machine learning models and pipelines for scalability, reliability, and performance
• Collaborate with cross-functional stakeholders to understand needs and deliver impactful solutions

Key Skills:

• Strong software engineering skills and experience with AI/ML development frameworks
• Deep knowledge of machine learning algorithms, techniques, and best practices
• Familiarity with data engineering, including data collection, cleaning, and preprocessing at scale
• Excellent problem-solving skills and ability to translate business needs into technical solutions

As artificial intelligence continues to reshape industries across the globe, the demand for skilled AI Engineers is skyrocketing. By building the technological bridges that connect the world of AI research with the realm of practical application, these innovators are unlocking the vast potential of intelligent machines to transform the way we live and work.

How to Prepare for High-Paying Machine Learning Roles

Ready to embark on a lucrative career in machine learning? To set yourself up for success, focus on three key areas: developing core technical skills, applying your knowledge through hands-on projects, and staying up-to-date with the latest advancements in the field.

1. Build a Strong Foundation

To excel in machine learning, you need a solid grasp of the fundamentals. Start by mastering programming languages like Python or R, and familiarize yourself with essential libraries like scikit-learn, TensorFlow, and PyTorch. Develop a deep understanding of probability, statistics, and linear algebra - the mathematical foundations of ML.

Looking for a comprehensive learning path? Check out our Machine Learning Engineer track. It covers crucial topics such as:

• Data preprocessing and feature engineering
• Supervised and unsupervised learning algorithms
• Deep learning and neural networks
• Model evaluation and optimization techniques

2. Explore the Real-World Projects

Theory is important, but the real magic happens when you apply your skills to solve actual problems. Engage in hands-on machine learning projects that mimic real-world scenarios. Tackle challenges like:

• Building a recommendation system for an e-commerce site
• Developing a predictive maintenance model for industrial equipment

Projects not only reinforce your understanding of ML concepts but also help you develop valuable problem-solving skills. Participate in Kaggle competitions, contribute to open-source projects, and engage with the vibrant ML community to learn from experts and peers alike.

3. Stay on the Cutting Edge

Machine learning is a rapidly evolving field, with new breakthroughs happening all the time. To stay competitive, you need to keep your finger on the pulse of the latest advancements and best practices.

Make a habit of reading influential publications like JMLR, following industry leaders on social media, and attending conferences and workshops. Engage in continuous learning through online courses, research papers, and side projects.

The key to success in high-paying machine learning roles is to build a strong foundation, apply your skills through real-world projects, and commit to ongoing learning. By focusing on these three areas, you'll position yourself for a thriving career at the forefront of artificial intelligence and data science.

How to Determine if a Machine Learning Role is Right for You

Thinking about pursuing a career in machine learning? Here's how to assess if an ML role aligns with your skills, interests, and aspirations.

Machine Learning Roles at a Glance

• Machine Learning Engineer: Builds and deploys ML models to solve real-world problems
• Data Scientist: Analyzes complex data to uncover insights and inform business decisions
• Software Engineer: Develops and maintains software systems and applications
• AI Research Scientist: Conducts cutting-edge research to advance the field of AI
• Natural Language Processing Engineer: Develops models and algorithms to process and analyze human language
• Business Intelligence Developer: Creates tools and systems to analyze business data and support decision-making
• AI Product Manager: Defines the strategy and roadmap for AI-powered products and features
• AI Engineer: Designs and implements AI systems and solutions

Is a Machine Learning Career Right for You? Key Questions to Consider

To determine if an ML role is a good fit, ask yourself:

• Are you fascinated by the potential of artificial intelligence to transform industries?
• Do you have a strong foundation in math, statistics, and programming?
• Are you a creative problem-solver who enjoys tackling complex challenges?
• Can you effectively communicate technical concepts to non-technical stakeholders?
• Are you committed to continuous learning in a rapidly evolving field?

Succeeding in machine learning roles requires a unique blend of technical skills, problem-solving abilities, and business acumen. According to a report by McKinsey, the most successful AI projects are those that align closely with business goals and involve cross-functional collaboration.

Ultimately, consider how your skills, passions, and career aspirations align with the responsibilities and opportunities of an ML role. Reflect on your motivation to harness the power of data and algorithms to drive innovation and solve real-world challenges.

Conclusion

Machine learning is revolutionizing industries worldwide. As businesses increasingly leverage AI, the demand for skilled ML professionals is rapidly growing. If you're passionate about using data to solve complex problems, now is the ideal time to start a career in machine learning. Dataquest offers comprehensive Machine Learning and Generative AI Fundamentals paths to help you become job-ready.

To excel in your machine learning journey, continuously update your knowledge, engage with the ML community, and apply your skills through real-world projects. Whether you're a student or a professional, developing machine learning expertise can elevate your career and place you at the forefront of the AI revolution.