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Machine Learning Engineer Jobs in Puerto Rico (NOW HIRING)

Responsibilities - Design and implement advanced AI and machine learning solutions - Analyze ... Engineering, Mathematics, Statistics, or a related quantitative field - At least 3 years of ...

... Machine Learning Specialty, or Google ML Engineer • Certified AI Professional (CAIP) or similar • Security engineering or technical certifications such as Security +, GIAC (GSEC, GCED, GCIH ...

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Machine Learning Engineer information

What engineers make $500,000?

Senior machine learning engineers with extensive experience, advanced skills in deep learning and data science, and often working in high-demand industries or companies can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially in tech giants or startups with significant funding.

What do machine learning engineers do?

Machine learning engineers develop algorithms and models that enable computers to learn from data and make predictions or decisions. They often work with large datasets, use programming languages like Python or Java, and utilize tools such as TensorFlow or PyTorch to build, test, and deploy machine learning systems in production environments.

What are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

Which 5 jobs will survive AI?

Machine Learning Engineers are likely to continue to be in demand as AI advances, as they develop and refine algorithms, models, and systems. Roles that require complex problem-solving, creativity, and domain expertise—such as healthcare professionals, data scientists, software developers, cybersecurity specialists, and AI ethics officers—are also expected to persist due to their reliance on human judgment and specialized knowledge. These jobs often involve skills that are difficult for AI to fully replicate or replace.

What Does a Machine Learning Engineer Do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What engineers make $300,000 a year?

Senior machine learning engineers and data scientists with extensive experience, advanced skills in deep learning, and proficiency with tools like TensorFlow or PyTorch can earn $300,000 or more annually, especially in high-cost-of-living areas or top tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their expertise and impact on business outcomes.

What are some common challenges faced by Machine Learning Engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What is the difference between Machine Learning Engineer vs Data Scientist?

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

What are the most commonly searched types of Machine Learning Engineer jobs in Puerto Rico? The most popular types of Machine Learning Engineer jobs in Puerto Rico are:
What job categories do people searching Machine Learning Engineer jobs in Puerto Rico look for? The top searched job categories for Machine Learning Engineer jobs in Puerto Rico are:
What are popular job titles related to Machine Learning Engineer jobs in PR? For Machine Learning Engineer jobs in PR, the most frequently searched job titles are:
Infographic showing various Machine Learning Engineer job openings in Puerto Rico as of July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.
AI and Machine Learning Engineer

AI and Machine Learning Engineer

Hewlett Packard Enterprise

Aguadilla, PR

Full-time

Posted 10 days ago


Hewlett Packard Enterprise rating

8.4

Company rating: 8.4 out of 10

Based on 26 frontline employees who took The Breakroom Quiz

34th of 156 rated electronics manufacturers


Job description

AI and Machine Learning EngineerThis role has been designed as 'Hybrid' with an expectation that you will work on average 2 days per week from an HPE office.

Who We Are:

Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today's complex world.Our culture thrives onfinding new and better ways to accelerate what's next.We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs.We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you.Open up opportunities with HPE.

Job Description:

We are seeking Senior AI/ML & Innovation Engineer who will be leading initiatives across Hybrid Cloud portfolio and thrives on a challenging and fast-paced environment.

Develops and programs integrated software algorithms to structure, analyze and leverage structured and unstructured data in product and systems applications. Can work with large scale computing frameworks, data analysis systems, and modeling environments.

Uses machine learning and statistical modeling techniques to improve product/system performance, data management, quality, and accuracy. Formulates descriptive, diagnostic, predictive and prescriptive insights/algorithms and translates technical specifications into code. Applies, optimizes and scales deep learning technologies and algorithms to give computers the capability to visualize, learn and respond to complex situations. Documents procedures for installation and maintenance, completes programming, performs testing and debugging, defines and monitors performance metrics. Contributes to the success of HPE by translating customer requirements and industry trends into AI/ML products, solutions, and systems improvement projects.

Contributions impact technical components of HPE products, solutions, or services regularly and sustainable. Applies advanced subject matter knowledge to solve complex business issues and is regarded as a subject matter expert. Provides expertise and partnership to functional and technical project teams and may participate in cross-functional initiatives. Exercises significant independent judgment to determine best method for achieving objectives. May provide team leadership and mentoring to others.

How you will make your mark:

  • Conducts research and stays up to date with the latest advancements in AI and machine learning technologies, frameworks, and algorithms. Explore and experiment with cutting-edge techniques to solve complex problems and improve existing models.

  • Collaborates with cross-functional teams to understand business requirements and design AI and machine learning solutions. Determine the appropriate algorithms, models, and frameworks to use and architect the overall system to ensure scalability, efficiency, and robustness.

  • Develops, implements, and optimizes machine learning models and algorithms. This includes data pre-processing, feature engineering, model selection, hyperparameter tuning, and training on large datasets. Continuously monitor and improve model performance and accuracy.

  • Deploys machine learning models into production environments, considering scalability, performance, and security considerations.

  • Integrate models with existing software systems and infrastructure, ensuring smooth operation and interoperability.

  • Monitors the performance of deployed models, collects relevant metrics, and analyzes data to identify areas for improvement. Based on insights gained from monitoring and analysis, fine-tune models, optimize algorithms, and enhance system performance.

  • Organizes and leads comprehensive design review sessions, driving discussions to align with project requirements and best practices. Mentor and provide feedback to junior and mid-level team members.

  • Works collaboratively with the engineering manager and team lead to set design and implementation standards, ensuring continuous improvement and alignment with project goals.

  • Regularly leads meetings, fostering a collaborative and productive team environment.

  • Has experience in providing technical leadership, mentorship, and guidance to junior team members.

  • Address and resolve challenges proactively.

  • Develops and delivers strategic presentations and reports to senior stakeholders, demonstrating a deep understanding of technical and business aspects. Provide insights and recommendations.

  • Applies and leverages data mining, data modeling, natural language processing, and machine learning to extract and analyze information from datasets.

About you:

  • Bachelor's or master's degree in computer science, engineering, data science, machine learning, artificial intelligence, or closely related quantitative discipline.

  • Typically, 4-7 years' experience.

  • Deep understanding of machine learning algorithms, such as linear regression, decision trees, support vector machines, random forests, deep learning models (e.g., neural networks), and reinforcement learning. Proficient in model selection, hyperparameter tuning, and evaluating model performance using appropriate metrics.

  • A strong foundation in mathematics and statistics. In-depth knowledge of linear algebra, calculus, probability theory, and statistical concepts. Understanding and developing complex machine learning models and algorithms.

  • Proficiency in programming languages such as Python, R, or Java is expected. Experience developing production-level code and familiarity with software engineering best practices, version control systems (e.g., Github), and software development methodologies are also required. Additionally, knowledge of libraries and frameworks like TensorFlow, PyTorch, sci-kit, and Keras is a plus.

  • Strong understanding of GitHub CoPilot, Cursor, N8N, vibe coding, Windsurf, and similar technologies

  • Experience in Cloud Infrastructure (AWS, Azure, etc)

  • Knowledge of Open Source, Linux, etc

  • Understanding of Devops, SRE

  • Advanced knowledge and experience in deep learning. Understanding advanced neural network architectures (e.g., convolutional neural networks, recurrent neural networks, transformers) and advanced techniques such as transfer learning, generative models, and optimization algorithms for deep learning.

  • Actively staying updated with the latest AI and machine learning research advancements. Experience conducting research, exploring emerging technologies, and identifying opportunities to apply state-of-the-art techniques to solve complex problems.

  • Must have excellent communication skills to collaborate with cross-functional teams and stakeholders effectively. Possess strong problem-solving and critical thinking abilities to guide projects, make strategic decisions, and solve complex technical challenges.

  • Strong programming skills, with expertise in Python, R, or Java, are necessary. Experience with popular machine learning frameworks and libraries like TensorFlow, PyTorch, or sci-kit is essential.

  • A deep understanding of statistical modeling, data mining, and data visualization is highly preferred.

Additional Skills:

Artificial Intelligence Technologies, Cross Domain Knowledge, Data Engineering, Data Science, Design Thinking, Development Fundamentals, Full Stack Development, IT Performance, Machine Learning Operations, Scalability Testing, Security-First Mindset

What We Can Offer You:

Health & Wellbeing

We strive to provide our team members and their loved ones with a comprehensive suite of benefits that supports their physical, financial and emotional wellbeing.

Personal & Professional Development

We also invest in your career because the better you are, the better we all are. We have specific programs catered to helping you reach any career goals you have - whether you want to become a knowledge expert in your field or apply your skills to another division.

Unconditional Inclusion

We are unconditionally inclusive in the way we work and celebrate individual uniqueness. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good.

Let's Stay Connected:

Follow @HPECareers on Instagram to see the latest on people, culture and tech at HPE.

#puertorico#hybridcloud

Job:

Engineering

Job Level:

TCP_03

HPE is an Equal Employment Opportunity/ Veterans/Disabled/LGBT employer. We do not discriminate on the basis of race, gender, or any other protected category, and all decisions we make are made on the basis of qualifications, merit, and business need. Our goal is to be one global team that is representative of our customers, in an inclusive environment where we can continue to innovate and grow together. Please click here: Equal Employment Opportunity.

Hewlett Packard Enterprise is EEO Protected Veteran/ Individual with Disabilities.

HPE will comply with all applicable laws related to employer use of arrest and conviction records, including laws requiring employers to consider for employment qualified applicants with criminal histories.

Recruitment Fraud Alert

We have become aware of an increase in fraudulent recruitment activities in which individuals impersonate our company or authorized recruitment agencies to offer fake employment opportunities. These scams may occur through false websites, emails, social media, or chat-based applications and often aim to obtain personal information or money. Please note that Hewlett Packard Enterprise (HPE), its direct and indirect subsidiaries and affiliated companies, and its authorized recruitment agencies/vendors will never charge a candidate a registration fee, hiring fee, or any other fee in connection with its recruitment and hiring process. We also never request personal information such as back account details, Social Security numbers, or national IDs via social media or chat applications.

All legitimate job opportunities will come through official company channels, and candidates are responsible for verifying the credentials of any third party claiming to represent the company. Any reliance on fraudulent communication is at the individual's own risk, and HPE disclaims legal liability for any resulting damages. If you suspect recruitment fraud, do not share personal information or make any payments and report the incident to your local authorities immediately.


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