Types of Machine Learning Engineer Jobs
Machine Learning Engineer - Research
A Machine Learning Engineer focused on research works on developing new algorithms and advancing the state-of-the-art in machine learning. They often collaborate with academic institutions and publish papers. Their work is more theoretical and experimental, pushing the boundaries of what is possible with machine learning. They may also prototype new models and test their feasibility. This role is ideal for those interested in innovation and deep technical challenges.
Machine Learning Engineer - Production
A Production Machine Learning Engineer specializes in deploying machine learning models into real-world applications. They focus on scalability, reliability, and performance of models in production environments. This role requires strong software engineering skills in addition to machine learning expertise. They work closely with DevOps and data engineering teams. Their main goal is to ensure that models deliver value in live systems.
Deep Learning Engineer
A Deep Learning Engineer is a specialized type of Machine Learning Engineer who focuses on neural networks and deep learning architectures. They work with large datasets and complex models such as CNNs, RNNs, and transformers. Their expertise is crucial for applications like computer vision, natural language processing, and speech recognition. They often require strong knowledge of GPU programming and distributed computing. This role is highly technical and in demand in cutting-edge AI fields.
Applied Machine Learning Engineer
An Applied Machine Learning Engineer focuses on using existing machine learning techniques to solve practical business problems. They work closely with product and business teams to understand requirements and deliver solutions. Their work involves feature engineering, model selection, and performance tuning. They often use off-the-shelf libraries and frameworks to accelerate development. This role bridges the gap between research and production.
Machine Learning Platform Engineer
A Machine Learning Platform Engineer builds and maintains the infrastructure required for machine learning workflows. They develop tools and platforms that enable data scientists and ML engineers to train, test, and deploy models efficiently. Their work involves automation, orchestration, and monitoring of ML pipelines. They need strong skills in cloud computing and software engineering. This role is essential for scaling machine learning efforts in large organizations.
Entry Level Job Titles
Junior Machine Learning Engineer
A Junior Machine Learning Engineer assists in building and deploying machine learning models under the supervision of senior engineers. They are responsible for data preprocessing, feature engineering, and basic model training. This role is ideal for recent graduates or those transitioning into machine learning from related fields. They often work on well-defined tasks and gradually take on more complex responsibilities. Strong programming and analytical skills are essential for this position.
Machine Learning Intern
A Machine Learning Intern is typically a student or recent graduate gaining hands-on experience in the field. They work on small projects or support ongoing initiatives, learning about data pipelines, model development, and evaluation. Interns receive mentorship from experienced engineers and are exposed to real-world machine learning challenges. This role is a stepping stone to a full-time position. It provides valuable industry exposure and skill development.
Data Scientist - Entry Level
An Entry Level Data Scientist often overlaps with junior machine learning roles, focusing on data analysis, visualization, and basic model building. They work with structured and unstructured data to extract insights and support decision-making. This role provides foundational experience in data handling and statistical modeling. It is a common entry point for those aiming to become machine learning engineers. Collaboration and communication skills are important for this position.
AI Engineer - Entry Level
An Entry Level AI Engineer works on implementing basic AI and machine learning solutions. They assist in coding, testing, and deploying simple models under guidance. This role helps build a strong foundation in AI concepts and practical application. They may also contribute to documentation and model evaluation. It is suitable for those starting their careers in artificial intelligence.
Research Assistant - Machine Learning
A Research Assistant in Machine Learning supports academic or industrial research projects. They help with literature reviews, data collection, and running experiments. This role is ideal for those interested in pursuing advanced studies or research careers. It provides exposure to cutting-edge techniques and collaboration with experienced researchers. Strong analytical and organizational skills are required.
Mid Level Job Titles
Machine Learning Engineer
A mid-level Machine Learning Engineer independently designs, develops, and deploys machine learning models. They are responsible for end-to-end project execution, from data preprocessing to model evaluation and deployment. This role requires a solid understanding of machine learning algorithms, software engineering, and data pipelines. They often mentor junior engineers and collaborate with cross-functional teams. Continuous learning and staying updated with the latest advancements are key aspects of this position.
Data Scientist - Machine Learning Focus
A Data Scientist with a focus on machine learning applies advanced statistical and machine learning techniques to solve business problems. They work on feature engineering, model selection, and performance optimization. This role involves close collaboration with business stakeholders to translate requirements into technical solutions. They are expected to communicate findings and recommendations effectively. Strong programming and analytical skills are essential.
AI Engineer
An AI Engineer at the mid-level develops and integrates AI solutions into products and services. They work on a variety of machine learning and deep learning projects, often involving natural language processing or computer vision. This role requires experience with AI frameworks and deployment tools. They contribute to the design and architecture of AI systems. Collaboration with product and engineering teams is common.
Machine Learning Specialist
A Machine Learning Specialist focuses on specific areas such as natural language processing, computer vision, or recommendation systems. They bring deep expertise in their chosen domain and drive innovation within the team. This role involves research, prototyping, and implementation of advanced models. They often present findings at conferences or internal meetings. Continuous skill development is important for this position.
Machine Learning Software Engineer
A Machine Learning Software Engineer combines software engineering best practices with machine learning expertise. They build robust, scalable, and maintainable ML systems. This role involves writing production-quality code, optimizing model performance, and ensuring system reliability. They work closely with data engineers and DevOps teams. Strong coding and system design skills are required.
Senior Level Job Titles
Senior Machine Learning Engineer
A Senior Machine Learning Engineer leads the design and implementation of complex machine learning systems. They mentor junior team members and set technical direction for projects. This role involves solving challenging problems, optimizing models, and ensuring scalability and reliability. They often collaborate with stakeholders to align technical solutions with business goals. Strong leadership and communication skills are essential.
Lead Machine Learning Engineer
A Lead Machine Learning Engineer oversees a team of engineers and drives the execution of machine learning projects. They are responsible for project planning, resource allocation, and technical decision-making. This role requires deep technical expertise and the ability to manage multiple projects simultaneously. They act as a bridge between technical teams and business stakeholders. Leadership and project management skills are crucial.
Principal Machine Learning Engineer
A Principal Machine Learning Engineer is a technical expert who sets the vision for machine learning initiatives within an organization. They lead research and development efforts, evaluate new technologies, and guide architectural decisions. This role involves mentoring other engineers and influencing company-wide strategies. They are recognized as thought leaders in the field. Strong innovation and strategic thinking skills are required.
Staff Machine Learning Engineer
A Staff Machine Learning Engineer is a senior technical contributor who drives innovation and best practices across teams. They work on high-impact projects and provide technical guidance to multiple teams. This role involves deep technical expertise, cross-team collaboration, and influencing organizational direction. They often represent the company at conferences and industry events. Strong problem-solving and communication skills are essential.
Machine Learning Architect
A Machine Learning Architect designs the overall architecture for machine learning systems and platforms. They ensure that solutions are scalable, maintainable, and aligned with business objectives. This role involves evaluating new tools and technologies, setting standards, and guiding implementation. They work closely with engineering, data, and product teams. Strong system design and leadership skills are required.
Director Level Job Titles
Director of Machine Learning
The Director of Machine Learning leads the machine learning function within an organization. They are responsible for setting the strategic direction, managing teams, and overseeing large-scale projects. This role involves collaborating with executive leadership to align machine learning initiatives with business goals. They ensure that the team has the resources and support needed to succeed. Strong leadership, communication, and strategic planning skills are essential.
Director of AI
The Director of AI oversees all artificial intelligence initiatives, including machine learning, deep learning, and related technologies. They manage multiple teams and projects, ensuring alignment with organizational objectives. This role involves setting vision, driving innovation, and representing the AI function at the executive level. They are responsible for talent development and resource allocation. Strong technical and business acumen are required.
Director of Data Science
The Director of Data Science leads data science and machine learning teams, driving the development of data-driven solutions. They set the vision for data science initiatives and ensure alignment with business strategy. This role involves managing budgets, hiring, and mentoring team members. They collaborate with other departments to maximize the impact of data science. Strong leadership and communication skills are essential.
Director of Engineering - Machine Learning
The Director of Engineering for Machine Learning manages engineering teams focused on building and deploying ML systems. They are responsible for technical strategy, project delivery, and team development. This role involves close collaboration with product, data, and business teams. They ensure that engineering practices are aligned with organizational goals. Strong technical and managerial skills are required.
Director of Research - Machine Learning
The Director of Research in Machine Learning leads research teams focused on advancing the field. They set research agendas, secure funding, and foster collaboration with academic and industry partners. This role involves publishing papers, presenting at conferences, and driving innovation. They mentor researchers and ensure the quality of research output. Strong research and leadership skills are essential.
VP Level Job Titles
Vice President of Machine Learning
The Vice President of Machine Learning is responsible for the overall machine learning strategy and execution at the organizational level. They oversee multiple teams and ensure alignment with business objectives. This role involves setting vision, managing budgets, and representing the function at the executive level. They drive innovation and ensure the successful delivery of machine learning projects. Strong leadership and strategic thinking skills are required.
Vice President of AI
The Vice President of AI leads all artificial intelligence initiatives, including machine learning, deep learning, and related technologies. They are responsible for setting the strategic direction, managing large teams, and ensuring business impact. This role involves close collaboration with other executives and external partners. They represent the organization in industry forums and conferences. Strong technical and business leadership skills are essential.
Vice President of Data Science
The Vice President of Data Science oversees data science and machine learning functions across the organization. They set the vision, manage resources, and ensure alignment with business goals. This role involves driving innovation, managing large teams, and representing the function at the executive level. They are responsible for talent development and organizational growth. Strong leadership and communication skills are required.
Vice President of Engineering - AI/ML
The Vice President of Engineering for AI/ML manages engineering teams focused on artificial intelligence and machine learning. They are responsible for technical strategy, project delivery, and team development. This role involves collaborating with other executives to drive business impact. They ensure that engineering practices are world-class and aligned with organizational goals. Strong technical and managerial skills are required.
Vice President of Research - AI/ML
The Vice President of Research for AI/ML leads research and innovation efforts in artificial intelligence and machine learning. They set research agendas, secure funding, and foster collaboration with academic and industry partners. This role involves publishing papers, presenting at conferences, and driving organizational innovation. They mentor researchers and ensure the quality of research output. Strong research and leadership skills are essential.
How to Advance Your Current Machine Learning Engineer Title
Gain Deep Technical Expertise
To advance as a Machine Learning Engineer, focus on mastering advanced machine learning algorithms, deep learning frameworks, and software engineering best practices. Continuously update your knowledge by taking online courses, attending workshops, and reading research papers. Building a strong portfolio of projects and contributions to open-source initiatives can also help. Seek feedback from peers and mentors to identify areas for improvement. Demonstrating technical excellence is key to moving up the career ladder.
Develop Leadership Skills
As you progress, leadership skills become increasingly important. Take on mentorship roles, lead small projects, and collaborate with cross-functional teams. Effective communication, project management, and the ability to influence others are crucial for senior roles. Consider pursuing certifications or training in leadership and management. Building a reputation as a reliable and inspiring team member will open up more opportunities.
Contribute to Business Impact
Align your work with business objectives and demonstrate the value of your machine learning solutions. Work closely with stakeholders to understand their needs and deliver impactful results. Quantify the business impact of your projects and communicate them effectively. This will help you gain visibility and recognition within the organization. Business acumen is highly valued in senior and leadership roles.
Expand Your Professional Network
Networking with professionals in the field can provide valuable insights and opportunities. Attend conferences, join professional organizations, and participate in online communities. Building relationships with industry leaders and peers can lead to collaborations, mentorship, and job opportunities. Networking also helps you stay updated with industry trends and best practices. A strong professional network is an asset for career advancement.
Pursue Advanced Education or Certifications
Consider pursuing a master's or PhD in machine learning, artificial intelligence, or a related field. Advanced degrees can open doors to research and leadership positions. Certifications in cloud computing, data engineering, or specific ML frameworks can also enhance your credentials. Continuous learning demonstrates your commitment to professional growth. It can differentiate you from other candidates in competitive job markets.
Similar Machine Learning Engineer Careers & Titles
Data Scientist
A Data Scientist analyzes and interprets complex data to help organizations make informed decisions. They use statistical methods, machine learning, and data visualization techniques. While there is overlap with machine learning engineers, data scientists often focus more on data analysis and less on model deployment. They work closely with business stakeholders to extract insights from data. This role is common in industries such as finance, healthcare, and technology.
AI Engineer
An AI Engineer develops and implements artificial intelligence solutions, including machine learning, deep learning, and natural language processing. They work on building intelligent systems that can automate tasks and solve complex problems. This role requires strong programming and problem-solving skills. AI Engineers often collaborate with data scientists and software engineers. Their work spans various industries, from robotics to customer service.
Deep Learning Engineer
A Deep Learning Engineer specializes in designing and training deep neural networks for tasks such as image recognition, speech processing, and natural language understanding. They work with large datasets and require expertise in frameworks like TensorFlow and PyTorch. This role is highly technical and in demand in fields like autonomous vehicles and healthcare. Deep Learning Engineers often push the boundaries of what is possible with AI. They collaborate with researchers and product teams to deliver innovative solutions.
Data Engineer
A Data Engineer designs, builds, and maintains the infrastructure required for data generation, storage, and processing. They ensure that data is accessible, reliable, and ready for analysis by data scientists and machine learning engineers. This role requires strong skills in database management, ETL processes, and cloud computing. Data Engineers play a critical role in enabling machine learning workflows. They often work closely with analytics and engineering teams.
Research Scientist - Machine Learning
A Research Scientist in Machine Learning focuses on advancing the field through original research and development. They publish papers, develop new algorithms, and contribute to the scientific community. This role is often found in academic institutions, research labs, and innovative tech companies. Research Scientists collaborate with engineers to translate research into practical applications. Strong analytical and problem-solving skills are essential.