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Graduate Machine Learning Engineer Jobs in Texas

Photon

Machine Learning Engineer - NJ

Addison, TX · On-site

$54 - $71.50/hr

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

Apple

Senior Machine Learning Engineer

Austin, TX · On-site

$121K - $160K/yr

... Machine Learning Engineer. This role will assist our Online Retail Decision Automation team by ... Preferred Qualifications PhD or Graduate degree with research/work experience using data science ...

Photon

Machine Learning Engineer - NJ

Addison, TX

$54 - $71.50/hr

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

Capital One Group

Lead Machine Learning Engineer

Plano, TX · On-site

$179 - $205/hr

R249230Lead Machine Learning Engineer**Join the Dealer Tech division within Capital One's Financial Services Technology group, where we develop and support cutting edge technological solutions that ...

New

Capital One

Lead Machine Learning Engineer

Plano, TX · On-site

$98K - $129K/yr

Lead Machine Learning Engineer Join the Dealer Tech division within Capital One's Financial Services Technology group, where we develop and support cutting edge technological solutions that ...

Socket.dev

Lead Machine Learning Engineer

Plano, TX · On-site

$179 - $205/hr

Lead Machine Learning Engineer Join the Dealer Tech division within Capital One's Financial Services Technology group, where we develop and support cutting edge technological solutions that ...

New

Rokt group

Senior Machine Learning Engineer

Austin, TX · On-site

$335 - $400/hr

We are hiring Senior Machine Learning Engineers We are hiring engineers with significant expertise in both machine learning and software engineering. You will be working with our engineering and ...

New

Capital One

Lead Machine Learning Engineer

Plano, TX · On-site

$98K - $129K/yr

Lead Machine Learning Engineer Join the Dealer Tech division within Capital One's Financial Services Technology group, where we develop and support cutting edge technological solutions that ...

Rokt

Senior Machine Learning Engineer

Austin, TX · On-site

$210 - $260/hr

We are hiring Senior Machine Learning Engineers We are hiring engineers with significant expertise in both machine learning and software engineering. You will be working with our engineering and ...

Apple

Senior Machine Learning Engineer

Austin, TX

$184K - $324K/yr

... Machine Learning Engineer. This role will assist our Online Retail Decision Automation team by ... Preferred Qualifications PhD or Graduate degree with research/work experience using data science ...

Apple

Senior Machine Learning Engineer

Austin, TX

$184K - $324K/yr

... Machine Learning Engineer. This role will assist our Online Retail Decision Automation team by ... Preferred Qualifications PhD or Graduate degree with research/work experience using data science ...

Apple

Senior Machine Learning Engineer

Austin, TX

$184K - $324K/yr

... Machine Learning Engineer. This role will assist our Online Retail Decision Automation team by ... Preferred Qualifications PhD or Graduate degree with research/work experience using data science ...

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Showing results 41-60

All JobsGraduate Machine Learning Engineer JobsGraduate Machine Learning Engineer Jobs in Texas

Graduate Machine Learning Engineer information

See Texas salary details

$29.3K

$120K

$180.3K

How much do graduate machine learning engineer jobs pay per year?

As of Aug 19, 2026, the average yearly pay for graduate machine learning engineer in Texas is $119,968.00, according to ZipRecruiter salary data. Most workers in this role earn between $94,600.00 and $144,400.00 per year, depending on experience, location, and employer.

What does a graduate machine learning engineer do?

A Graduate Machine Learning Engineer is an entry-level professional who designs, develops, and tests machine learning models and algorithms. They work with data scientists and engineers to preprocess data, train models, and deploy solutions to solve real-world problems. Their responsibilities often include coding in languages like Python, using libraries such as TensorFlow or PyTorch, and staying updated with the latest advancements in machine learning. This role serves as a starting point for a career in AI, providing hands-on experience in building and optimizing intelligent systems.

What are some common challenges faced by graduate machine learning engineers during their first year, and how can they overcome them?

Graduate Machine Learning Engineers often encounter challenges such as bridging the gap between academic knowledge and real-world application, working with large or messy datasets, and learning to collaborate within cross-functional teams. Adapting to production-level code standards and understanding existing codebases can also be demanding. To overcome these hurdles, it's helpful to seek mentorship from experienced colleagues, actively participate in code reviews, and invest time in learning best practices for data preprocessing and model deployment. Embracing continuous learning and open communication will ease the transition into the professional environment.

What are the key skills and qualifications needed to thrive as a graduate machine learning engineer, and why are they important?

To thrive as a Graduate Machine Learning Engineer, you need a solid foundation in computer science, mathematics (especially statistics and linear algebra), and proficiency in programming languages like Python, often supported by a relevant degree. Familiarity with machine learning frameworks (such as TensorFlow or PyTorch), version control systems (like Git), and experience with cloud platforms or data management tools are typically expected. Strong analytical thinking, problem-solving abilities, and effective communication help you collaborate and translate complex concepts into practical solutions. These skills and qualities are crucial for developing robust models, integrating them into real-world applications, and contributing effectively to multidisciplinary teams.

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

AspectGraduate Machine Learning EngineerData Scientist
Required CredentialsBachelor's or Master's in CS, Data Science, or related field; some internshipsBachelor's or Master's in Statistics, Data Science, or related field; often with experience
Work EnvironmentDeveloping ML models, coding, testing algorithmsAnalyzing data, creating visualizations, deriving insights
Employer & Industry UsageTech companies, startups, research labsFinance, healthcare, tech, consulting firms

While both roles involve working with data and algorithms, Graduate Machine Learning Engineers focus on developing and deploying machine learning models, often requiring coding and technical skills. Data Scientists analyze data to extract insights and inform decisions. The roles overlap in skills but differ in primary responsibilities and focus areas.

What are popular job titles related to Graduate Machine Learning Engineer jobs in Texas?

For Graduate Machine Learning Engineer jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Graduate Machine Learning Engineer jobs in Texas look for?

The top searched job categories for Graduate Machine Learning Engineer jobs in Texas are:

Graduate Machine Learning Engineer jobs near you
Infographic showing various Graduate Machine Learning Engineer 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 $119,968 per year, or $57.7 per hour.

Machine Learning Engineer - NJ

Photon

Addison, TX • On-site

$54 - $71.50/hr

Full-time

Re-posted 12 days ago

Job description


Summary:
We are seeking a Machine Learning Engineer to design and develop robust analytics models using statistical and machine learning algorithms. In this role, you will work closely with product and engineering teams to solve complex business problems, identify data-driven opportunities, and create personalized experiences for customers. You will be responsible for building end-to-end machine learning solutions, implementing models in production, and working with various data frameworks and tools such as Python, Spark, and Databricks.
Key Responsibilities: Analytics Model Development:
  • Analyze use cases and design appropriate analytics models using statistical and machine learning algorithms tailored to specific business requirements.
  • Develop machine learning algorithms to drive personalized customer experiences and provide actionable business insights.
  • Apply expertise in data mining and machine learning techniques, including forecasting, prediction, segmentation, recommendation, and fraud detection.

Data Engineering and Preparation:
  • Extend and augment company data with third-party data to enrich analytics capabilities.
  • Enhance data collection procedures to include necessary information for building analytics systems.
  • Prepare raw data for analysis, including cleaning, imputing missing values, and standardizing data formats using Python data frameworks (e.g., Pandas, NumPy).

Machine Learning Model Implementation:
  • Implement machine learning models, considering both performance and scalability using tools like PySpark in Databricks.
  • Design and build infrastructure to facilitate large-scale data analytics and experimentation.
  • Work with tools like Jupyter Notebooks for data exploration and model development.

What We're Looking For:
  • Educational Background: Undergraduate or Graduate degree in Computer Science, Mathematics, Physics, or related fields. A PhD is preferred but not necessary.
  • Experience:
    • At least 5 years of experience in data analytics, with a strong understanding of core statistical algorithms such as classification and regression analysis.
    • High-level knowledge of analytics use cases such as language analysis, assortment optimization, promotional planning, dynamic pricing, markdown optimization, labor scheduling, and optimization.
  • Technical Skills:
    • Strong experience with Python-based machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch).
    • Proficiency in using analytics platforms like Databricks for large-scale data processing.
    • At least 4 years of continuous experience with Spark, particularly PySpark implementation.
    • Hands-on experience with data processing and analysis tools such as Pandas, NumPy, and Jupyter Notebooks.

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