What Does a Machine Learning Engineer Do?

A machine learning (ML) engineer is a software engineering role with an emphasis on building smart systems that can be trained from data and make decisions without someone coding every single rule. ML engineers sit at the intersection of data science, data engineering, and software development, turning large datasets into real‑world solutions.

ML engineers often team up with data scientists and data engineers to prepare and clean data, then use that data to train predictive models and machine learning systems for things like automation, decision‑making, and analytics. Successful ML engineers typically have strong foundations in computer science, math, programming, and general problem solving, enabling them to deliver scalable, data-driven solutions.

See how ML engineers turn data into smart, real-world solutions and learn what a machine learning engineer really does on a day-to-day basis — from building predictive models and managing big data to improving model performance and scaling artificial intelligence (AI) systems.

What do machine learning engineers do?

The regular tasks of an ML engineer can vary significantly depending on their company, team size, and industry. Generally, their function is to design, build, and maintain machine learning techniques systems that can be trained and improved over time using data.

Common tasks include:

• Data preprocessing. Cleaning and formatting large datasets (big data) so they’re usable by ML models, paying special attention to data quality and data modeling.
• Model development. Creating, training, and testing machine learning algorithms and predictive models using frameworks like TensorFlow, PyTorch, and programming languages like Python or Java (this includes feature engineering and tuning hyperparameters for better model performance).
• System integration. Embedding ML models into real‑world apps, APIs, and software tools to improve user experience.
• Scaling. Engineering infrastructure for scalability to ensure machine learning systems work reliably in production.
• Performance monitoring. Tracking model performance with metrics like accuracy, precision, and recall, as well as retraining or updating models as needed to maintain high performance.
• Validation and testing. Running A/B tests, cross‑validation, and other statistical methods to confirm that ML models and predictive models function as expected in production.
• Collaboration. Working closely with data analysts, research scientists, product teams, and stakeholders to hit business goals and bridge the gap between data analysis results and production machine learning systems.
• Documentation. Writing clear code documentation, workflows, and reports to communicate the details, process, and results of the work.

Key skills and qualifications for ML engineers

Machine learning engineers who stand out among similar job titles (such as AI Engineers) have the technical skills to get the job done, as well as the communication abilities to work with cross-functional teams.

Look for skills that include:

• Strong programming knowledge. Python, Java, and C++ are go‑to languages for ML due to their flexibility and well-maintained ecosystems.
• Data structures and algorithms. A strong grasp of common ML models helps you implement and optimize solutions.
• Machine learning algorithms. The building blocks of predictive models include supervised, unsupervised, and reinforcement learning (e.g., clustering, regression, and decision trees).
• Math and statistics. Linear algebra, calculus, and probability are used to build and understand models, engineer features, and create data models.
• Software development practices. Knowing Git, agile workflows, version control, and having machine learning operations (MLOps) experience helps when deploying models, managing machine learning systems, and ensuring scalability across the board.
• Deep learning frameworks. Understanding tools like TensorFlow, PyTorch, and Keras is critical when you’re working with neural networks.
• APIs and system design. ML often needs to plug into related apps, services, and data pipelines, so knowing how to create REST APIs, think about system design, and integrate with production workflows matters.
• Communication and teamwork. Explaining how models work to people in nontechnical roles and collaborating across teams with data analysts, engineers, research scientists, and stakeholders are important parts of the job description.
• Advanced degree or certifications. A master’s degree in computer science, data science, or machine learning adds credibility. Certifications like Google Professional ML Engineer, AWS Certified Machine Learning Specialty, or Microsoft Azure AI Engineer Associate also show your skill set.

Typical use cases for machine learning engineers

ML engineers in all kinds of industries solve real‑world problems and optimize systems for efficiency, scalability, and better decision‑making.

Here are a few types of companies that employ ML engineers or engage freelancers in the field:

• Tech giants. Companies like Amazon, Google, Meta, and Microsoft work with ML engineers for large‑scale AI initiatives and machine learning systems.
• Health care. ML engineers build tools for diagnostics, drug discovery, and patient monitoring, where predictive models can help improve outcomes.
• Finance. Use cases include fraud detection, credit scoring, algorithmic trading, and systems that rely on automation, large datasets, data engineering workflows, and feature engineering to work.
• Retail and e‑commerce. Recommendation engines, inventory forecasting, and dynamic pricing are all tasks that involve machine learning algorithms in production.
• Transportation and logistics. Self‑driving technologies, route optimization, and predictive maintenance rely on ML engineers designing systems that can scale and adapt over time.
• Startups. AI‑driven startups often bring on ML engineers to build new tools or integrate machine learning into software-as-a-service (SaaS) products, focusing on problem‑solving and rapid iteration.

Tools and platforms used by ML engineers

ML engineers use a whole ecosystem of tools to handle everything from data engineering to deployment and monitoring of machine learning systems.

Some common tools with machine learning applications are:

• Data management. Apache Spark, Hadoop, Airflow, and Snowflake for working with big data, analyzing data quality, and performing data modeling.
• Model training. Jupyter Notebooks, Colab, and SageMaker enable experimentation, model development, and real‑world testing.
• Versioning and MLOps. Git, DVC, MLflow, and Kubeflow help with tracking, collaboration, and deployments. These tools help ensure scalability and reliable model performance.
• Visualization. Matplotlib, Seaborn, and Tableau help in data analysis, showing stakeholders how models work and what insights they deliver.
• APIs and integration. REST APIs and gRPC let you embed models into apps and services. That means your machine learning systems actually function in real use cases.

These tools let teams scale workflows, maintain data quality, and optimize model performance across projects.

Machine learning engineer costs

Machine learning engineers are among the top‑paid roles in tech. In the U.S., they average over $140,000 a year, with high earners making $230,000 or more. On Upwork, ML engineers charge $50 to $200 per hour, depending on experience, skill set, and project complexity.

As more businesses adopt automation, artificial intelligence, and machine learning systems, demand for ML engineers is expected to grow significantly. 

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FAQs

Thinking about hiring a machine learning engineer? Here are key insights to help you better understand the role, common use cases, and what to consider when evaluating candidates.

What’s the difference between an ML engineer and a data analyst?

A data analyst focuses on data analysis, which consists of looking at data to find trends and insights. An ML engineer builds machine learning systems that train computer algorithms from data, build predictive models, and make decisions automatically.

How is machine learning used in edge computing?

ML models run directly on devices like smartphones or “Internet of Things” (IoT) sensors to enable faster response times, better privacy, and real‑world automation without constant cloud access.

Do ML engineers work on voice assistants or chatbots?

Yes. ML engineers help build NLP models and neural networks that power chatbots and virtual assistants, tying software engineering with data science and AI.

Can machine learning models be biased?

Yes. If your training data is biased or has bad data quality, your model will likely show bias. ML engineers must use feature engineering, validation, and system design to minimize unfair outcomes and maintain model performance.

Upwork is not affiliated with and does not sponsor or endorse any of the tools or services discussed in this article. These tools and services are provided only as potential options, and each reader and company should take the time needed to adequately analyze and determine the tools or services that would best fit their specific needs and situation.

The rates and information provided in this article are based on current data and industry sources available at the time of publication. Freelance rates can vary depending on factors such as experience, location, project scope, and market conditions. Readers are encouraged to conduct their own research to confirm current rates and trends, as this information may change over time.

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