Machine Learning Infrastructure Engineer Job Description [Updated for 2025]

In the era of artificial intelligence, machine learning infrastructure engineers have taken center stage.

As technology continues to advance, the demand for skilled professionals who can build, enhance, and secure our machine learning infrastructure is escalating.

But what exactly is expected from a machine learning infrastructure engineer?

Whether you are:

• A job seeker trying to understand the core responsibilities of this role,
• A hiring manager outlining the perfect candidate,
• Or simply interested in the complexities of machine learning infrastructure,

You’ve come to the right place.

Today, we are rolling out a tailor-made machine learning infrastructure engineer job description template, designed for seamless posting on job boards or career sites.

Let’s dive in.

Machine Learning Infrastructure Engineer Duties and Responsibilities

Machine Learning Infrastructure Engineers are responsible for creating and maintaining the platforms that allow machine learning code to be developed, tested, and implemented.

They use their knowledge in machine learning, data science, and software engineering to optimize systems and infrastructure.

They have the following duties and responsibilities:

• Develop and maintain scalable data pipelines and build out new API integrations to support continuing increases in data volume and complexity.
• Collaborate with data and machine learning scientists to understand their needs and devise possible solutions.
• Work on improving the reliability, efficiency, and maintainability of data systems.
• Implement systems for tracking data quality and consistency.
• Design, construct, install, test and maintain highly scalable data management systems.
• Translate complex functional and technical requirements into detailed design.
• Ensure systems meet business requirements and industry practices.
• Integrate up-and-coming data management and software engineering technologies into existing data structures.
• Develop set processes for data mining, data modeling, and data production.
• Research opportunities for data acquisition and new uses for existing data.
• Develop machine learning models according to requirements.
• Ensure the infrastructure used for data storage and processing is secure and efficient.

 

Machine Learning Infrastructure Engineer Job Description Template

Job Brief

We are seeking a dedicated Machine Learning Infrastructure Engineer to develop and maintain the machine learning platform and infrastructure within our organization.

Responsibilities will include creating machine learning models, improving infrastructure design, and working with data scientists to implement machine learning algorithms.

Our ideal candidate will have a strong understanding of machine learning principles, experience working with large datasets, and a solid background in software engineering.

Ultimately, the role of the Machine Learning Infrastructure Engineer is to build high-performing, scalable, and secure machine learning systems.

 

Responsibilities

• Develop, maintain, and improve machine learning infrastructure
• Collaborate with data scientists to understand their needs and ensure system consistency
• Create and maintain machine learning models with a focus on performance, reliability, and scalability
• Implement machine learning algorithms and libraries
• Collaborate with the data engineering team to build data and model pipelines
• Manage A/B testing frameworks and model performance evaluation systems
• Manage infrastructure costs and efficiency
• Continuously research and implement best practices for maintaining machine learning infrastructure

 

Qualifications

• Proven work experience as a Machine Learning Engineer, Data Engineer, or similar role
• Experience with machine learning frameworks (like Keras or PyTorch) and libraries (like scikit-learn)
• Understanding of data structures, data modeling, and software architecture
• Proficiency in Python, Java, or another programming language
• Experience with cloud services (like AWS or Google Cloud)
• Excellent problem-solving and communication skills
• Experience with database systems, both SQL and NoSQL
• BSc degree in Computer Science, Engineering or relevant field; Master’s degree in Data Science or other quantitative field is preferred

 

Benefits

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

 

Additional Information

• Job Title: Machine Learning Infrastructure Engineer
• Work Environment: This role requires working in an office setting with options for remote work. Minimal travel may be required for training or team meetings.
• Reporting Structure: Reports to the Head of Machine Learning or Data Engineering Manager.
• Salary: Salary is based upon candidate experience and qualifications, as well as market and business considerations.
• Pay Range: $105,000 minimum to $175,000 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 Engineer Do?

Machine Learning Infrastructure Engineers work for a variety of industries, including tech companies, research institutions, and data-driven corporations.

They can also be self-employed or work as consultants.

They collaborate closely with data scientists, machine learning engineers, and software developers to build and maintain the infrastructure and systems needed for machine learning applications.

This includes setting up data pipelines, scaling models to handle larger volumes of data, and ensuring that the systems are robust and reliable.

Their role also involves reviewing and deploying machine learning models, managing the data flow, and creating tools to automate and optimize these processes.

They are responsible for ensuring the efficiency, speed, quality, and scalability of machine learning systems.

Machine Learning Infrastructure Engineers often work on improving the performance of machine learning algorithms and must make sure that the infrastructure can handle the computational complexity of these algorithms.

They also need to ensure the security and privacy of the data used in machine learning applications.

Furthermore, they are often tasked with staying updated on the latest advancements in machine learning and infrastructure technology, to ensure that their organization’s systems remain at the forefront of the field.

 

Machine Learning Infrastructure Engineer Qualifications and Skills

A Machine Learning Infrastructure Engineer must possess a combination of technical expertise, soft skills, and a deep understanding of machine learning to develop and maintain machine learning infrastructure.

These include:

• Strong expertise in machine learning concepts, tools, and applications to build, test, and deploy machine learning models.
• Excellent programming skills, preferably in Python, Java, or C++, to develop and optimize machine learning algorithms.
• Experience with machine learning frameworks such as TensorFlow, PyTorch, or Keras, and cloud platforms like AWS, Google Cloud, or Azure for deploying machine learning models.
• Strong knowledge of data structures, data modeling, and software architecture to handle large amounts of data and to design scalable, efficient machine learning infrastructure.
• Good understanding of DevOps practices such as continuous integration/continuous deployment (CI/CD), and tools like Docker or Kubernetes, to automate the deployment, scaling, and management of machine learning models.
• Ability to work collaboratively with data scientists, software engineers, and other team members using effective interpersonal skills.
• Problem-solving skills to troubleshoot and resolve issues that may arise in the machine learning infrastructure.
• Attention to detail and organization to manage multiple projects simultaneously while ensuring accuracy and efficiency.

 

Machine Learning Infrastructure Engineer Experience Requirements

Entry-level Machine Learning Infrastructure Engineers usually have 1 to 2 years of experience, often gained through internships, research projects, or part-time roles in the field of machine learning, data science, or software development.

These professionals can also gain practical experience through roles such as Data Analyst, Software Developer, or other IT-related roles where they get the chance to apply machine learning principles.

Familiarity with programming languages like Python, Java, or C++, and tools like TensorFlow, PyTorch, or Keras is typically expected.

Candidates with more than 3 years of experience often develop their technical skills and knowledge in machine learning algorithms, cloud computing, and system design in roles such as Machine Learning Engineer or Data Scientist.

They often have a deeper understanding of infrastructure requirements for deploying machine learning models, and experience in handling big data.

Those with more than 5 years of experience may have some leadership experience, and are likely proficient in architecting, developing, and maintaining machine learning infrastructure.

They would typically have experience with distributed systems, and an in-depth understanding of the full machine learning life cycle.

At this level, they may be ready for a managerial or team-lead position.

Candidates for senior roles may also be required to have experience with specific infrastructure platforms such as AWS, GCP, or Azure, and be proficient in DevOps tools and methodologies.

They should also be capable of collaborating with multiple teams, and guiding the implementation of machine learning projects.

 

Machine Learning Infrastructure Engineer Education and Training Requirements

Machine Learning Infrastructure Engineers typically possess a bachelor’s degree in computer science, information technology, mathematics, or a related field.

They should have an in-depth understanding of algorithms, data structures, and machine learning concepts, as well as proficiency in programming languages such as Python, R, or Java.

In addition to a bachelor’s degree, many employers prefer candidates with a master’s degree or Ph.D. in machine learning, data science, artificial intelligence, or a related field.

This role also requires an understanding of software engineering practices, cloud computing, and deployment models.

Familiarity with tools such as TensorFlow, PyTorch, Keras, and cloud platforms like AWS, Google Cloud, or Azure is often required.

Certifications, such as Microsoft Certified: Azure AI Engineer Associate or Google Cloud Certified – Professional Data Engineer, can also enhance a candidate’s resume and demonstrate their commitment to staying updated in this rapidly evolving field.

Ongoing training and self-learning are crucial as the field of machine learning is continually evolving, introducing new technologies, frameworks, and methodologies.

 

Machine Learning Infrastructure Engineer Salary Expectations

The average salary for a Machine Learning Infrastructure Engineer is around $120,474 (USD) per year.

This can vary significantly based on factors such as experience, qualifications, the complexity of the tasks involved, and the location of the job.

With increased experience and expertise, the salary can potentially rise significantly.

 

Machine Learning Infrastructure Engineer Job Description FAQs

What skills does a Machine Learning Infrastructure Engineer need?

Machine Learning Infrastructure Engineers need a strong foundation in computer science including algorithms, data structures, and software development.

Proficiency in programming languages such as Python, Java, or C++ is essential.

They should have experience with machine learning frameworks like TensorFlow or PyTorch and cloud platforms like AWS or Google Cloud.

Understanding of data engineering, data science, and machine learning concepts is also necessary.

 

Do Machine Learning Infrastructure Engineers need a degree?

Most Machine Learning Infrastructure Engineers hold a bachelor’s degree in Computer Science, Statistics, Mathematics, or a related field.

However, due to the specialized nature of this role, many professionals also have a master’s degree or PhD in these fields.

Some companies might accept equivalent work experience in place of formal education.

 

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

Look for a strong background in computer science, data engineering, and machine learning.

Experience with relevant programming languages, frameworks, and cloud platforms should be highlighted.

Any certifications related to machine learning or cloud platforms can be a bonus.

Also, consider projects or previous roles that involved designing and implementing machine learning systems at scale.

 

What qualities make a good Machine Learning Infrastructure Engineer?

A good Machine Learning Infrastructure Engineer should have strong analytical skills and the ability to solve complex problems.

They should be detail-oriented, capable of debugging and optimizing code, and possess the ability to work in a team.

A continuous learning mindset is essential given the rapidly evolving field of machine learning.

 

Is it difficult to hire Machine Learning Infrastructure Engineers?

Hiring Machine Learning Infrastructure Engineers can be challenging due to the specialized skills required.

The role demands a combination of skills in computer science, data engineering, and machine learning, which is not easy to find.

Offering competitive salaries, growth opportunities, and challenging projects can attract qualified candidates.

 

Conclusion

There you have it.

We’ve delved into the intricacies of what it truly means to be a machine learning infrastructure engineer.

Surprise, surprise.

It’s not just about programming algorithms.

It’s about orchestrating the symphony of artificial intelligence, one well-structured data pipeline at a time.

With our definitive machine learning infrastructure engineer job description template and real-world examples, you’re primed to make your next big career leap.

But why put a full stop there?

Dig deeper with our job description generator. It’s your compass to architecting precise job postings or refining your resume to impeccable standards.

Just remember:

Every algorithm is a component of a larger, intelligent system.

Let’s construct that future. Hand in hand.

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