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Internship Graduate Machine Learning Jobs in Texas

Mariana Minerals

Machine Learning Engineer

Houston, TX · On-site

Required : • 0-4 years of experience (including internships or research) in machine learning, reinforcement learning, or scientific computing--or a strong recent graduate with demonstrated project ...

Apple

Machine Learning Engineer

Austin, TX · On-site

We are looking for a passionate, highly motivated, and hands-on applied Machine Learning Engineer ... output PhD or Graduate degree with research/work experience utilizing data science techniques ...

Apple

Machine Learning Engineer

Austin, TX

$132K - $244K/yr

Engage in continuous learning and development, staying up-to-date with the latest advances in machine learning and software engineering Preferred Qualifications PhD or Graduate degree with research ...

Apple

Machine Learning Engineer

Austin, TX

$132K - $244K/yr

Engage in continuous learning and development, staying up-to-date with the latest advances in machine learning and software engineering Preferred Qualifications PhD or Graduate degree with research ...

Apple

Machine Learning Engineer

Austin, TX

$132K - $244K/yr

Engage in continuous learning and development, staying up-to-date with the latest advances in machine learning and software engineering Preferred Qualifications PhD or Graduate degree with research ...

Photon

Machine Learning Engineer - NJ

Dallas, TX

Machine Learning Engineer We are seeking a Machine Learning Engineer to design and develop robust ... Undergraduate or Graduate degree in Computer Science, Mathematics, Physics, or related fields. A ...

Capital One

Lead Machine Learning Engineer

Plano, TX · On-site +1

$98K - $129K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Internship experience does not apply) * At least 4 years of experience programming with Python ...

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Internship Graduate Machine Learning information

How much do ML interns get paid?

Machine Learning interns typically earn between $15 and $30 per hour, depending on the company, location, and level of experience. Internships often last 10 to 12 weeks and may include additional benefits such as mentorship and skill development opportunities.

What is the difference between Internship Graduate Machine Learning vs Data Analyst?

AspectInternship Graduate Machine LearningData Analyst
Required CredentialsDegree in Computer Science, Data Science, or related field; basic knowledge of programming and statisticsDegree in Statistics, Mathematics, or related field; proficiency in data visualization and analysis tools
Work EnvironmentTech companies, research labs, startups; project-based, collaborative teamsBusiness, finance, marketing sectors; focus on reporting and data interpretation
Employer & Industry UsageUsed in tech, AI, and research industries for developing machine learning modelsCommon in corporate, finance, and consulting firms for data-driven decision making

While both roles involve working with data, an Internship Graduate Machine Learning focuses on developing algorithms and models using programming skills, often in tech environments. In contrast, a Data Analyst emphasizes interpreting data, creating reports, and supporting business decisions. The roles overlap in data handling but differ in technical depth and application focus.

What are the big 4 internships?

The big 4 internships typically refer to internship programs offered by the four largest professional services firms: Deloitte, PricewaterhouseCoopers (PwC), Ernst & Young (EY), and KPMG. These internships provide opportunities in areas such as audit, consulting, tax, and advisory services, often targeting students pursuing degrees in business, finance, or related fields. They are highly competitive and often include training, mentorship, and potential pathways to full-time employment.

What are the key skills and qualifications needed to thrive as an Internship Graduate in Machine Learning, and why are they important?

To thrive as an Internship Graduate in Machine Learning, you typically need a strong background in mathematics, programming (especially Python), and familiarity with algorithms and data structures, often supported by coursework or a degree in computer science, statistics, or a related field. Hands-on experience with machine learning frameworks like TensorFlow or PyTorch, and knowledge of tools such as Jupyter Notebooks and version control systems like Git, are highly valued. Curiosity, problem-solving, teamwork, and effective communication are crucial soft skills to excel in collaborative and innovative environments. These competencies enable interns to contribute to real-world projects, adapt to fast-changing technologies, and communicate findings clearly within interdisciplinary teams.

What are Internship Graduate Machine Learning positions?

Internship Graduate Machine Learning positions are entry-level roles designed for recent graduates or students who have completed coursework in machine learning, data science, or related fields. These internships provide hands-on experience working with real-world data, building and testing machine learning models, and collaborating with experienced professionals. Interns gain exposure to industry-standard tools and techniques, helping them bridge the gap between academic learning and practical application. Such positions are valuable for building a portfolio, networking, and enhancing job prospects in the rapidly growing field of artificial intelligence.

What types of projects do Internship Graduate Machine Learning roles typically involve, and how are responsibilities structured within the team?

Internship Graduate Machine Learning roles often focus on supporting ongoing research or development projects, such as building predictive models, cleaning and analyzing data, or prototyping algorithms. Interns usually collaborate closely with data scientists and engineers, contributing to specific project milestones while learning best practices in model development and deployment. Responsibilities are often structured to allow for mentorship and feedback, with interns participating in regular team meetings, code reviews, and brainstorming sessions. This collaborative environment provides valuable exposure to real-world machine learning workflows and helps interns build both technical and soft skills relevant to the field.

Which 5 jobs will survive AI?

Jobs that require complex human judgment, creativity, emotional intelligence, and specialized skills—such as healthcare professionals, data scientists, software developers, educators, and skilled tradespeople—are more likely to persist despite AI advancements. These roles often involve tasks that are difficult for AI to fully replicate or automate, especially when combined with continuous learning and adaptability.

Is PG in AI worth it?

A postgraduate degree in AI can enhance qualifications for machine learning internship roles by providing advanced knowledge of algorithms, data analysis, and programming skills. It may improve job prospects and salary potential but is not always mandatory, as practical experience and skills in tools like Python and TensorFlow are highly valued in the field.
What are the most commonly searched types of Graduate Machine Learning jobs in Texas? The most popular types of Graduate Machine Learning jobs in Texas are:
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What cities in Texas are hiring for Internship Graduate Machine Learning jobs? Cities in Texas with the most Internship Graduate Machine Learning job openings:
Internship Graduate Machine Learning jobs near you

Machine Learning Engineer

Mariana Minerals

Houston, TX • On-site

Full-time

Posted 3 days ago

Job description

Job Summary:
Mariana Minerals is a software-first, vertically integrated minerals company focused on supplying critical minerals for modern energy and technology. They are seeking a Machine Learning Engineer to develop and improve machine learning systems for mineral refining facilities, working with real data to enhance operational efficiency.
Responsibilities:
• Run reinforcement learning experiments in our physically realistic simulators of mineral processing operations, and help turn the results into better controllers.
• Build and refine pieces of our training environments—reward functions, observations, and action logic—with guidance from senior engineers.
• Train control models, track and interpret their performance, and dig into why a model underperforms.
• Help close the gap between simulation and reality by comparing model behavior against real plant data and flagging where the physics diverges.
• Write clean, well-tested code and contribute to the services that put models into production.
• Partner with process and chemistry experts to understand the unit operations you're modeling.
Qualifications:
Required:
• 0–4 years of experience (including internships or research) in machine learning, reinforcement learning, or scientific computing—or a strong recent graduate with demonstrated project depth.
• Solid grounding in machine learning fundamentals, with working knowledge of modern deep learning; exposure to reinforcement learning is a strong plus.
• Proficiency in Python and comfort reading and debugging an existing codebase.
• Curiosity about physical, industrial systems and eagerness to learn chemistry and process engineering from experts who will challenge your assumptions.
• A self-starter who asks good questions, ships, and escalates blockers early.
Company:
Mariana Minerals is a software-first, vertically integrated minerals company focused on supplying the minerals critical to modern energy, AI, and defense technologies. Founded in , the company is headquartered in San Francisco, CA, US, , with a team of 51-200 employees. The company is currently Growth Stage.

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