Machine Learning Infrastructure Developer Job Description [Updated for 2025]

In the era of artificial intelligence, the focus on Machine Learning Infrastructure Developers has never been greater.

As technology continues to evolve, so does the demand for skilled professionals who can construct, advance, and secure our machine learning infrastructure.

But let’s delve deeper: What is really expected from a Machine Learning Infrastructure Developer?

Whether you are:

• A job seeker attempting to understand the core of this role,
• A hiring manager creating the profile of the perfect candidate,
• Or simply fascinated by the inner workings of machine learning infrastructure development,

You’ve come to the right place.

Today, we present a customizable Machine Learning Infrastructure Developer job description template, designed for easy posting on job boards or career sites.

Let’s dive right in.

Machine Learning Infrastructure Developer Duties and Responsibilities

Machine Learning Infrastructure Developers work with complex data structures and algorithms to build scalable machine learning systems.

They have a robust understanding of computer science, data structures, and machine learning algorithms.

Their duties and responsibilities include:

• Designing, implementing, and maintaining machine learning infrastructure
• Working closely with data scientists and other stakeholders to understand their needs and translate them into functional infrastructure requirements
• Building and optimizing machine learning algorithms, data pipelines, and architectures
• Ensuring the scalability and reliability of machine learning systems
• Developing machine learning infrastructure components and tools for deployment, monitoring, and operations
• Performing ad-hoc analyses and presenting results in a clear manner
• Implementing data collection processes and managing and ensuring data quality
• Developing tools to monitor system performance and reliability
• Maintaining up-to-date knowledge of technology trends and developments in the field of machine learning
• Documenting system configurations, algorithms, and processes for reference and future development

 

Machine Learning Infrastructure Developer Job Description Template

Job Brief

We are seeking a skilled Machine Learning Infrastructure Developer to join our team.

This role will involve designing, developing, and maintaining infrastructure for running machine learning models in production.

The Machine Learning Infrastructure Developer’s responsibilities include understanding machine learning workflows, developing tools and platforms for machine learning operations (MLOps), ensuring scalability, and improving efficiency of machine learning systems.

Our ideal candidates are well-versed with machine learning frameworks and have experience with cloud computing platforms.

 

Responsibilities

• Design, develop and maintain machine learning infrastructure.
• Ensure scalability and efficiency of machine learning systems.
• Collaborate with data scientists to understand machine learning workflows.
• Develop tools and platforms for machine learning operations (MLOps).
• Ensure model quality and data consistency.
• Monitor and troubleshoot machine learning systems and infrastructure.
• Develop machine learning validation and testing protocols.
• Stay up-to-date with latest industry trends in machine learning and infrastructure development.

 

Qualifications

• Proven work experience as a Machine Learning Infrastructure Developer, Machine Learning Engineer or similar role.
• Experience with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn).
• Familiarity with cloud services (AWS, Google Cloud, Azure).
• Good understanding of machine learning algorithms and principles.
• Experience with database systems, both SQL and NoSQL.
• Proficiency in Python, Java, or another programming language.
• Excellent problem-solving and communication skills.
• BSc degree in Computer Science, Engineering or a relevant field; Master’s degree in Data Science or Artificial Intelligence is a plus.

 

Benefits

• 401(k)
• Health insurance
• Dental insurance
• Retirement plan
• Paid time off
• Professional development assistance

 

Additional Information

• Job Title: Machine Learning Infrastructure Developer
• Work Environment: This position is based in a office setting with options for remote work. Some travel may be required for team meetings or client consultations.
• Reporting Structure: Reports to the Head of Machine Learning or AI Development Manager.
• Salary: Salary is based upon candidate experience and qualifications, as well as market and business considerations.
• Pay Range: $120,000 minimum to $220,000 maximum
• Location: [City, State] (specify the location or indicate if remote)
• Employment Type: Full-time
• Equal Opportunity Statement: We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
• Application Instructions: Please submit your resume and a cover letter outlining your qualifications and experience to [email address or application portal].

 

What Does a Machine Learning Infrastructure Developer Do?

Machine Learning Infrastructure Developers work primarily in the tech industry, although their skills can be applied in a variety of fields that use machine learning, such as healthcare, finance, and transportation.

Their main responsibility is to build and maintain the systems and platforms that enable machine learning models to be trained, tested, and deployed.

They are also responsible for scaling these systems to handle large volumes of data and ensure that they perform optimally.

Often, they collaborate with data scientists and machine learning engineers to understand their needs and provide the necessary infrastructure.

This can involve selecting the right hardware and software, setting up data pipelines, and implementing tools and frameworks for model training and deployment.

In addition, they are responsible for ensuring that the infrastructure is robust, reliable, and secure.

This can involve implementing monitoring systems, troubleshooting issues, and carrying out performance tuning.

Machine Learning Infrastructure Developers also need to stay up to date with the latest advancements in machine learning and infrastructure technologies, and be ready to adapt their systems as needed.

This could involve introducing new tools and practices, or upgrading existing systems to handle new types of machine learning models or larger amounts of data.

Lastly, they may also play a role in the design and implementation of machine learning models, particularly in terms of selecting the most efficient algorithms and data structures, and optimizing the performance of these models on the available infrastructure.

 

Machine Learning Infrastructure Developer Qualifications and Skills

A Machine Learning Infrastructure Developer should have skills and qualifications that align with the requirements of this specialized field, such as:

• Strong understanding and experience with Machine Learning algorithms, data structures, and performance optimization techniques.
• Proficiency in using programming languages such as Python, Java, or C++ to develop ML models and applications.
• Ability to use Machine Learning frameworks and libraries such as TensorFlow, PyTorch, Keras, or Scikit-Learn.
• Experience with cloud computing services such as AWS, Google Cloud, or Azure for deploying and maintaining Machine Learning models.
• Strong analytical and problem-solving skills to identify issues, propose solutions and optimizations in ML models and applications.
• Understanding of software development methodologies and tools, including version control systems like Git.
• Experience in using data visualization tools and libraries to effectively communicate findings and results.
• Strong attention to detail and the ability to work effectively on multiple projects simultaneously.
• Excellent communication skills to collaborate with cross-functional teams, including data scientists, engineers, and product managers.
• Understanding of data privacy and ethics when handling sensitive data.

 

Machine Learning Infrastructure Developer Experience Requirements

An entry-level Machine Learning Infrastructure Developer usually has 1 to 2 years of experience, often gained through internships or part-time roles in machine learning or data science.

These professionals can also gain on-the-job experience in roles such as Data Analyst, Software Developer, or roles related to Big Data.

Candidates with 3 to 5 years of experience are often expected to have developed their technical skills and knowledge in machine learning platforms and tools.

They should have a practical understanding of machine learning algorithms, cloud platforms, data pipelines, and be comfortable with programming languages such as Python, R, or Java.

Professionals with more than 5 years of experience in the field are often considered for senior roles.

They should have comprehensive knowledge about the design and implementation of machine learning models and infrastructure, and have experience with distributed systems.

They may also have some leadership experience, making them suitable for supervisory or managerial roles.

In addition, a Machine Learning Infrastructure Developer should have a solid understanding of statistics and mathematical concepts, and be experienced in problem-solving and critical thinking.

Professional certification in machine learning or a related field is a plus.

 

Machine Learning Infrastructure Developer Education and Training Requirements

Machine Learning Infrastructure Developers typically hold a bachelor’s degree in computer science, data science, statistics, or a related field.

A strong foundation in mathematics, particularly linear algebra, calculus, and probability, as well as fluency in programming languages such as Python, R, or Java, is crucial.

To be more competitive in the job market, many professionals go on to earn a master’s degree or PhD in machine learning, data science, or artificial intelligence.

Such advanced degrees provide more in-depth knowledge and practical skills in designing, developing, and maintaining machine learning models and infrastructure.

These professionals often need a good understanding of data structures, algorithms, and software engineering principles.

Knowledge of machine learning frameworks like TensorFlow, PyTorch, or Keras is also necessary.

Moreover, there are numerous certifications related to machine learning and data science, such as the Certified Analytics Professional (CAP) or Microsoft Certified: Azure AI Engineer Associate, that can enhance a candidate’s credentials.

On-the-job experience with infrastructure development, cloud computing platforms like AWS or Google Cloud, and databases, both SQL and NoSQL, is highly advantageous.

Lastly, due to the ever-evolving nature of the field, a Machine Learning Infrastructure Developer should be committed to continual learning, staying up-to-date with the latest algorithms, tools, and technologies.

 

Machine Learning Infrastructure Developer Salary Expectations

A Machine Learning Infrastructure Developer can expect to earn an average salary of $130,000 (USD) per year.

This figure, however, can fluctuate based on various factors such as professional experience in the field, educational background, and the geographical location of the job.

The type of industry and the size of the employing company can also significantly impact the salary.

 

Machine Learning Infrastructure Developer Job Description FAQs

What skills does a Machine Learning Infrastructure Developer need?

Machine Learning Infrastructure Developers need a strong knowledge of machine learning algorithms and principles, as well as experience with deep learning frameworks such as TensorFlow or PyTorch.

They should be proficient in programming languages such as Python, and have a strong understanding of data structures, data modeling, and software architecture.

Additionally, they need skills in cloud platforms like AWS, Google Cloud, or Azure, and should have the ability to design and implement machine learning infrastructure and pipelines.

 

Do Machine Learning Infrastructure Developers need a degree?

Most Machine Learning Infrastructure Developer roles require a bachelor’s degree in computer science, data science, statistics, or a related field.

Many employers prefer candidates with a master’s degree or Ph.D. in these fields.

However, some companies may consider equivalent work experience or other industry-specific certifications.

 

What should you look for in a Machine Learning Infrastructure Developer resume?

In addition to their educational background, look for experience in machine learning, cloud technologies, and data engineering.

They should have proven experience with machine learning models, data processing, and designing and deploying machine learning solutions in a production environment.

Experience with specific tools and languages, such as Python, SQL, TensorFlow, and cloud platforms like AWS, is also essential.

 

What qualities make a good Machine Learning Infrastructure Developer?

A good Machine Learning Infrastructure Developer is analytical and problem-solving, capable of using complex data to create efficient solutions.

They should have a deep understanding of machine learning algorithms and how to implement them into practical applications.

They should have excellent communication skills to work effectively within a team and explain complex concepts to non-technical stakeholders.

A passion for continuous learning is also vital due to the rapidly evolving nature of the field.

 

Is it difficult to hire Machine Learning Infrastructure Developers?

Given the specialized nature of the role and the high demand for these skills in the current job market, recruiting Machine Learning Infrastructure Developers can be challenging.

Companies need to offer competitive salaries, continuous learning opportunities, and interesting projects that use cutting-edge technology to attract and retain these professionals.

 

Conclusion

There you have it!

Today, we unraveled the intricate details of being a machine learning infrastructure developer.

Surprising, isn’t it?

It’s not just about algorithms and data structures.

It’s about creating the backbone of our AI-driven future, one machine learning model at a time.

Armed with our detailed machine learning infrastructure developer job description template and real-world examples, you’re ready to step into the future.

But there’s more!

Dig deeper with our job description generator. It’s your ultimate tool to creating spot-on job listings or refining your resume to perfection.

Remember:

Each machine learning algorithm is a piece of a larger, interconnected network.

Let’s build that AI-powered future. Together.

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