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Entry Level Machine Learning Jobs in Texas (NOW HIRING)

Entry Level Technologist

Houston, TX · On-site

$24.25 - $33.50/hr

As an Entry-Level Technology Consultant at Sogeti, you will join one of our core practices based on ... Explore emerging tech-from artificial intelligence/machine learning to cloud-native engineering ...

Entry Level Technologist

Houston, TX · On-site

$24.25 - $33.50/hr

As an Entry-Level Technology Consultant at Sogeti, you will join one of our core practices based on ... Explore emerging tech-from artificial intelligence/machine learning to cloud-native engineering ...

Showing results 21-40

Entry Level Machine Learning information

See Texas salary details

$11

$16

$20

How much do entry level machine learning jobs pay per hour?

As of Aug 21, 2026, the average hourly pay for entry level machine learning in Texas is $16.27, according to ZipRecruiter salary data. Most workers in this role earn between $14.57 and $17.69 per hour, depending on experience, location, and employer.

What are entry level machine learning jobs?

Entry-level machine learning jobs focus on creating and using software for the development of artificial intelligence (AI). In this role, you may help program computer software, engineer mechanical solutions, help develop learning objectives, and use analytics to determine whether or not the technology created is meeting development goals. Many entry-level machine learning jobs focus on particular parts of the industry. For example, some companies focus on surveillance and intelligence, while others are creating technology for self-driving vehicles. Employers often use this position as a type of extended learning period to help you develop your skills before you start taking responsibility for major projects.

What types of projects can an entry level machine learning professional expect to work on in their first year?

As an entry-level machine learning professional, you’ll typically start by supporting more senior data scientists and engineers with tasks such as data cleaning, exploratory data analysis, and building baseline models. You may work on pilot projects like developing recommendation systems, automating simple classification tasks, or contributing to model evaluation and performance tuning. Collaboration with cross-functional teams—including software engineers, product managers, and domain experts—is common, providing valuable exposure to real-world business problems and laying a foundation for more complex responsibilities as you gain experience.

What are the key skills and qualifications needed to thrive as an entry level machine learning engineer, and why are they important?

To thrive as an Entry Level Machine Learning Engineer, you need a solid background in mathematics, statistics, and programming (especially in Python), typically supported by a degree in computer science or a related field. Familiarity with machine learning frameworks like TensorFlow or PyTorch, version control systems like Git, and data analysis libraries is commonly required. Strong problem-solving abilities, curiosity, and effective communication skills help differentiate candidates in collaborative and fast-evolving environments. These skills and qualifications are essential for building, testing, and improving machine learning models that drive innovation and business value.

What is the difference between Entry Level Machine Learning vs Data Analyst?

AspectEntry Level Machine LearningData Analyst
Required CredentialsBachelor's in CS, Math, or related; some knowledge of programming and statisticsBachelor's in Statistics, Math, or related; proficiency in Excel, SQL, and data visualization tools
Work EnvironmentTech companies, startups, research labs; focus on developing models and algorithmsBusiness, finance, marketing; focus on interpreting data and generating reports
Employer & Industry UsageTech, e-commerce, healthcare; roles involve building predictive modelsRetail, finance, consulting; roles involve analyzing data trends and insights

Entry Level Machine Learning roles focus on developing algorithms and models using programming and statistical skills, often in tech-driven environments. Data Analysts interpret and visualize data to support business decisions, typically using tools like Excel and SQL. While both roles require analytical skills, Machine Learning positions emphasize coding and model development, whereas Data Analysts focus on data interpretation and reporting.

How to get into entry level machine learning with no experience?

Entry level machine learning roles typically require foundational knowledge in programming, statistics, and data analysis. Gaining skills through online courses, practicing with projects, and learning tools like Python, TensorFlow, or scikit-learn can help build a portfolio. Internships, certifications, and participating in competitions like Kaggle can also improve your chances of entering the field without prior experience.

What are the most commonly searched types of Machine Learning jobs in Texas?

The most popular types of Machine Learning jobs in Texas are:

What are popular job titles related to Entry Level Machine Learning jobs in Texas?

For Entry Level Machine Learning jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Entry Level Machine Learning jobs in Texas look for?

The top searched job categories for Entry Level Machine Learning jobs in Texas are:

What cities in Texas are hiring for Entry Level Machine Learning jobs?

Cities in Texas with the most Entry Level Machine Learning job openings:

Infographic showing various Entry Level Machine Learning job openings in Texas as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 20% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $33,844 per year, or $16.3 per hour.

Applied Data Solutions Program, Software Engineering (Full-Time Opportunities)

Apple

Austin, TX

$122K - $184K/yr

Full-time

Medical, Dental, Retirement

Posted 7 days ago


Apple rating

8.0

Company rating: 8.0 out of 10

Based on 677 frontline employees who took The Breakroom Quiz

7th of 30 rated technology retailers


Job description

Do you love the challenge of solving complex problems that can have a direct and meaningful impact on the company? Do you want to be part of a supportive team that’s constantly learning and having fun while solving tough business problems? We’d love to talk to you if you do!
Join the Applied Data Solutions Program (ADSP) and make a positive impact on one of the most influential technology leaders in the industry. You’ll be tasked with building sophisticated, scalable and robust architecture, tools, data products, and critical data pipelines that are optimized for rapid business intelligence, data analysis, and data science. You will work with teams across Apple, using data analysis and predictive modeling techniques, to define, build, deploy, and maintain end-to-end operational solutions that have a direct and measurable impact to the company and our customers. The enormous scale and complexity of the problems and our data present exciting opportunities for pushing the limits of existing data science methods.
Description
The ADSP provides entry-level talent with practical work experience through project-based rotations on various teams within Finance. Upon completion, ADSPs move into non-rotating technical roles within Apple. We have a variety of opportunities for software engineers, data scientists, and machine learning engineers. Responsibilities may include:
• Support our business partners and optimizes the customer experience by delivering data-driven solutions that mitigate fraud, improve security, and optimize efficiency.
• Work closely with Machine Learning Engineers and other Software Engineers to lead the design and implementation of scalable, easy-to-use systems and tools.
• Engage with stakeholders to translate ambiguous business problems into technical solutions, including finding opportunities, breaking them into solvable segments, defining requirements, assessing level of effort, etc.
• Work cooperatively to design data science-driven solutions, balancing the utility of tried-and-true techniques and the benefits of custom solutions.
• Develop self-service tools and automation to improve data engineering efficiency and self-service analytics.
• Create reporting and monitor decisioning quality to maintain operational and business metric health.
• Translate ambiguous business problems into technical solutions by working collaboratively with business partners.
• Develop, deploy, and operationally support machine-learning models that take real-time and forensic action against a variety of threats to Apple’s ecosystem.
• Apply data science to core finance processes to automate and elevate capability, forecasting, accruals, accounting entries prep, IA process, risk mitigation, etc.
Preferred Qualifications
Demonstrate ability to think holistically about system structures and interactions in order to anticipate technical, business, and customer impact.
Experience with ML platform provisioning and optimization (e.g., Dataiku, Ray, GPU-based training environments).
Demonstrate ability to think holistically about system structures and interactions in order to anticipate technical, business, and customer impact.
Minimum Qualifications
Undergraduate or graduate degree in Computer Science, Data Science, Data Analytics, Machine Learning or related field.
Proficient in at least one programming language (Python/Scala/Java preferred).
Practical experience (acquired through work, independent projects, or academic research) in deploying machine learning solutions to answer real-world questions.
Practical experience (acquired through work, independent projects, or academic research) with implementing data science-related applications in a programming language such as Python, Scala, or Java.
Effective communication skills to translate complex concepts and analysis into concise, business-focused solutions.
Previous Apple FDP or ADSP internship is required.
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $122,700 and $184,800, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

What Apple employees say

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About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976