Types of AI Jobs
AI Research Scientist
AI Research Scientists focus on developing new algorithms and advancing the field of artificial intelligence. They often work in academic, corporate, or research lab settings. Their work involves publishing papers, attending conferences, and collaborating with other researchers. They may specialize in areas such as natural language processing, computer vision, or reinforcement learning. Their contributions drive innovation and set the direction for future AI technologies.
Machine Learning Engineer
Machine Learning Engineers design, build, and deploy machine learning models into production systems. They work closely with data scientists and software engineers to ensure models are scalable and efficient. Their responsibilities include data preprocessing, model selection, and performance tuning. They often use programming languages like Python and frameworks such as TensorFlow or PyTorch. Their work is crucial for turning AI research into practical applications.
Data Scientist
Data Scientists analyze large datasets to extract insights and build predictive models. They use statistical methods, machine learning, and data visualization tools to solve business problems. Their role often involves data cleaning, feature engineering, and communicating results to stakeholders. They work in various industries, including finance, healthcare, and technology. Their expertise bridges the gap between raw data and actionable intelligence.
AI Product Manager
AI Product Managers oversee the development and deployment of AI-powered products. They define product vision, gather requirements, and coordinate between engineering, design, and business teams. Their role requires a blend of technical knowledge and business acumen. They ensure that AI solutions align with user needs and organizational goals. Their leadership is key to successful AI product launches.
AI Solutions Architect
AI Solutions Architects design end-to-end AI systems tailored to specific business needs. They evaluate technologies, create system architectures, and guide implementation teams. Their work involves understanding both technical and business requirements. They often interact with clients to ensure solutions are feasible and scalable. Their expertise ensures that AI projects are delivered successfully and efficiently.
Entry Level Job Titles
AI Intern
AI Interns assist with research, data preparation, and model development under the supervision of experienced professionals. They gain hands-on experience with AI tools and techniques. Their tasks may include data labeling, running experiments, and documenting results. Internships provide valuable exposure to real-world AI projects. This role is ideal for students or recent graduates looking to start a career in AI.
Junior Data Scientist
Junior Data Scientists support senior team members in analyzing data and building models. They perform data cleaning, exploratory analysis, and basic machine learning tasks. They often work on well-defined problems and receive guidance from more experienced colleagues. This role helps them develop technical and analytical skills. It serves as a stepping stone to more advanced positions in AI.
Machine Learning Engineer I
Machine Learning Engineer I is an entry-level position focused on implementing and testing machine learning models. They assist in data preprocessing, model training, and evaluation. They work closely with senior engineers to learn best practices and industry standards. This role provides foundational experience in deploying AI solutions. It is suitable for recent graduates with a background in computer science or related fields.
AI Analyst
AI Analysts collect, process, and analyze data to support AI initiatives. They use statistical tools and basic machine learning techniques to generate insights. Their work often involves preparing reports and visualizations for stakeholders. They collaborate with data scientists and engineers on various projects. This role is a good entry point for those interested in AI and analytics.
Research Assistant (AI)
Research Assistants in AI support academic or industrial research projects. They help with literature reviews, experiment setup, and data collection. Their responsibilities may include coding, running simulations, and summarizing findings. This position offers exposure to cutting-edge AI research. It is ideal for those considering graduate studies or a research-oriented career in AI.
Mid Level Job Titles
Machine Learning Engineer II
Machine Learning Engineer II takes on more complex projects and may lead small teams. They design, implement, and optimize machine learning models for production use. Their responsibilities include feature engineering, model evaluation, and system integration. They collaborate with cross-functional teams to deliver AI solutions. This role requires a solid understanding of both theory and practical application.
Data Scientist II
Data Scientist II handles larger datasets and more challenging analytical problems. They develop advanced models, conduct experiments, and interpret results for business impact. They may mentor junior team members and contribute to best practices. Their work often involves collaborating with product and engineering teams. This position is suited for professionals with a few years of experience in data science.
AI Software Engineer
AI Software Engineers develop software applications that incorporate AI and machine learning components. They work on integrating models into products, optimizing code, and ensuring system reliability. Their role requires strong programming skills and knowledge of AI frameworks. They often collaborate with data scientists and product managers. This position bridges the gap between AI research and software development.
Applied Scientist
Applied Scientists focus on translating AI research into practical applications. They design experiments, build prototypes, and validate solutions in real-world settings. Their work involves both theoretical and hands-on problem-solving. They often publish findings and contribute to intellectual property. This role is ideal for those who enjoy both research and application.
AI Project Manager
AI Project Managers oversee the planning and execution of AI projects. They coordinate teams, manage timelines, and ensure deliverables meet quality standards. Their responsibilities include risk management, resource allocation, and stakeholder communication. They need a good understanding of AI technologies and project management methodologies. This role is crucial for delivering AI projects on time and within budget.
Senior Level Job Titles
Senior Machine Learning Engineer
Senior Machine Learning Engineers lead the design and deployment of complex machine learning systems. They mentor junior engineers, review code, and set technical standards. Their work involves solving high-impact problems and optimizing models for performance and scalability. They often collaborate with research teams to implement cutting-edge techniques. This role requires deep expertise in machine learning and software engineering.
Senior Data Scientist
Senior Data Scientists lead analytical projects and drive data-driven decision-making. They develop sophisticated models, conduct advanced analyses, and present findings to executives. They mentor junior data scientists and contribute to strategic planning. Their expertise is critical for tackling the most challenging business problems. This position is typically held by professionals with significant experience in data science.
AI Technical Lead
AI Technical Leads oversee the technical direction of AI projects and teams. They set architectural standards, review designs, and ensure best practices are followed. Their role involves hands-on coding, technical mentorship, and cross-team collaboration. They are responsible for the overall quality and success of AI solutions. This position requires strong leadership and deep technical knowledge.
Principal AI Scientist
Principal AI Scientists drive innovation and set research agendas for AI teams. They lead high-impact projects, publish influential papers, and represent their organization at conferences. Their work shapes the future direction of AI research and development. They often collaborate with external partners and academic institutions. This role is reserved for recognized experts in the field.
AI Solutions Architect (Senior)
Senior AI Solutions Architects design and oversee the implementation of large-scale AI systems. They work with stakeholders to define requirements and ensure solutions meet business goals. Their responsibilities include evaluating new technologies, guiding technical teams, and ensuring system scalability. They play a key role in the success of enterprise AI initiatives. This position requires extensive experience in both AI and systems architecture.
Director Level Job Titles
Director of AI
The Director of AI leads the AI strategy and oversees all AI-related projects within an organization. They manage teams of researchers, engineers, and product managers. Their responsibilities include setting research agendas, allocating resources, and ensuring alignment with business objectives. They represent the AI function to executive leadership and external partners. This role requires a blend of technical expertise and strategic vision.
Director of Machine Learning
The Director of Machine Learning is responsible for guiding the development and deployment of machine learning solutions. They oversee multiple teams and ensure projects are delivered on time and within budget. Their role involves setting technical standards, mentoring leaders, and driving innovation. They collaborate with other departments to integrate machine learning into products and services. This position is key to scaling AI capabilities across the organization.
Director of Data Science
The Director of Data Science leads data science teams and sets the vision for data-driven decision-making. They oversee the development of predictive models, analytics platforms, and data strategies. Their responsibilities include talent development, project prioritization, and stakeholder management. They ensure that data science initiatives deliver measurable business value. This role requires strong leadership and deep expertise in analytics.
Director of AI Engineering
The Director of AI Engineering manages the engineering teams responsible for building AI systems. They ensure that AI solutions are robust, scalable, and maintainable. Their role involves overseeing software development, system integration, and technical operations. They work closely with research and product teams to deliver innovative AI products. This position requires extensive experience in both engineering and AI.
Director of Applied AI
The Director of Applied AI focuses on translating AI research into real-world applications. They lead teams that develop and deploy AI solutions across various business units. Their responsibilities include project management, stakeholder engagement, and technology transfer. They ensure that AI innovations are effectively implemented and deliver business impact. This role bridges the gap between research and practical deployment.
VP Level Job Titles
Vice President of AI
The Vice President of AI sets the overall vision and strategy for AI initiatives at the executive level. They oversee all AI teams and ensure alignment with organizational goals. Their responsibilities include budget management, talent acquisition, and external partnerships. They represent the company in industry forums and drive thought leadership. This role requires a combination of technical depth and executive leadership.
VP of Machine Learning
The VP of Machine Learning leads the organization's machine learning strategy and operations. They manage large teams and oversee the development of advanced ML solutions. Their role involves setting long-term goals, securing resources, and fostering innovation. They collaborate with other executives to integrate ML into core business processes. This position is critical for organizations aiming to be leaders in AI.
VP of Data Science
The VP of Data Science is responsible for the strategic direction of data science initiatives. They oversee multiple teams and ensure that data-driven insights inform business decisions. Their responsibilities include setting analytics priorities, managing budgets, and building external partnerships. They play a key role in shaping the organization's data culture. This role requires extensive experience in analytics and executive management.
VP of AI Engineering
The VP of AI Engineering leads the engineering function for AI products and platforms. They are responsible for technical strategy, team leadership, and delivery of scalable AI solutions. Their role involves cross-functional collaboration and driving operational excellence. They ensure that engineering practices support rapid innovation and high-quality outcomes. This position is essential for organizations with large-scale AI deployments.
VP of Applied AI
The VP of Applied AI focuses on leveraging AI to solve real-world business problems. They oversee teams that develop and implement AI applications across the organization. Their responsibilities include strategic planning, resource allocation, and stakeholder engagement. They ensure that AI investments deliver measurable value. This role is key for organizations seeking to maximize the impact of AI.
How to Advance Your Current AI Title
Gain Advanced Technical Skills
To advance in an AI career, continuously develop your technical expertise in areas such as machine learning, deep learning, and data engineering. Take online courses, attend workshops, and earn relevant certifications. Master popular programming languages and AI frameworks. Stay updated with the latest research and industry trends. Building a strong technical foundation is essential for moving into more senior roles.
Build a Strong Portfolio
Work on real-world AI projects and contribute to open-source initiatives. Showcase your work through a portfolio or GitHub repository. Participate in competitions like Kaggle to demonstrate your skills. A strong portfolio helps you stand out to employers and shows your ability to apply AI techniques to practical problems. It also provides talking points for interviews and networking.
Network with Industry Professionals
Attend AI conferences, meetups, and webinars to connect with other professionals in the field. Join online communities and participate in discussions. Networking can lead to mentorship opportunities, job referrals, and collaborations. Building relationships with industry leaders can provide valuable insights and open doors to new opportunities. Active networking is crucial for career growth in AI.
Pursue Advanced Education
Consider pursuing a master's or PhD in AI, machine learning, or a related field. Advanced degrees can open doors to research and leadership positions. They also provide opportunities to work on cutting-edge projects and publish research. Many senior and executive roles in AI require or prefer candidates with advanced education. Continuous learning is key to staying competitive in the field.
Develop Leadership and Communication Skills
As you progress in your AI career, strong leadership and communication skills become increasingly important. Take on mentorship or project management roles to gain experience. Learn to communicate complex technical concepts to non-technical stakeholders. Effective leadership and communication are essential for advancing to senior, director, or executive positions. These skills complement your technical expertise and enable you to drive impact at a higher level.
Similar AI Careers & Titles
Robotics Engineer
Robotics Engineers design, build, and program robots that often incorporate AI technologies. They work on both hardware and software aspects of robotic systems. Their role involves developing algorithms for perception, planning, and control. They collaborate with AI specialists to integrate machine learning into robotics applications. This job is closely related to AI, especially in fields like autonomous vehicles and industrial automation.
Computer Vision Engineer
Computer Vision Engineers develop algorithms that enable computers to interpret and process visual information. They work on tasks such as image recognition, object detection, and video analysis. Their work is heavily based on AI and machine learning techniques. They often collaborate with data scientists and software engineers. This role is essential in industries like healthcare, automotive, and security.
Natural Language Processing (NLP) Engineer
NLP Engineers focus on building systems that understand and generate human language. They develop models for tasks like speech recognition, sentiment analysis, and machine translation. Their work relies on AI, linguistics, and computer science. They often work in tech companies, research labs, or startups. This role is critical for applications such as chatbots, virtual assistants, and language-based analytics.
Data Engineer
Data Engineers design and maintain the infrastructure needed for data collection, storage, and processing. They build pipelines that support AI and machine learning workflows. Their work ensures that data is accessible, reliable, and ready for analysis. They collaborate with data scientists and analysts to enable data-driven decision-making. This role is foundational for any organization leveraging AI.
Business Intelligence (BI) Analyst
BI Analysts use data analysis and visualization tools to help organizations make informed decisions. They gather, process, and interpret data from various sources. While their focus is more on analytics than AI, they often work closely with AI teams. Their insights support strategic planning and operational improvements. This role is important for organizations seeking to leverage data for competitive advantage.