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Junior Machine Learning Engineer Jobs in Addison, TX

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Machine Learning Engineer

Frisco, TX · On-site

$140 - $190/hr

Overview Quarterhill is seeking a Machine Learning Engineer to join our forward-thinking team building the next generation of Intelligent Transportation Systems . In this role, you will design ...

Quarterhill

Machine Learning Engineer

Frisco, TX · On-site

Overview Quarterhill is seeking a Machine Learning Engineer to join our forward-thinking team building the next generation of Intelligent Transportation Systems . In this role, you will design ...

SuperMom's LLC

Machine Learning Engineer II

Irving, TX · On-site

$120 - $180/hr

As a Machine Learning Engineer II at 7-Eleven, you will collaborate with cross-functional teams and work closely with senior architects to implement scalable and efficient data solutions. Your ...

7-ELEVEN

Machine Learning Engineer II

Irving, TX · On-site

As a Machine Learning Engineer II at 7-Eleven, you will collaborate with cross-functional teams and work closely with senior architects to implement scalable and efficient data solutions. Your ...

7-Eleven

Machine Learning Engineer II

Irving, TX · On-site

As a Machine Learning Engineer II at 7-Eleven, you will collaborate with cross-functional teams and work closely with senior architects to implement scalable and efficient data solutions. Your ...

Capital One

Lead Machine Learning Engineer

Plano, TX · On-site

$98K - $129K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You ...

Capital One

Lead Machine Learning Engineer

Plano, TX

$98K - $130K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You ...

Yum! Brands

Machine Learning Engineer II

Plano, TX · On-site

As a Machine Learning Engineer, you will play a crucial role in developing and deploying cutting-edge machine learning models and solutions to enhance various aspects of our business operations, from ...

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 an Agile team dedicated to productionizing machine learning applications and systems at scale. You ...

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All JobsMachine Learning Engineer JobsJunior Machine Learning Engineer JobsJunior Machine Learning Engineer Jobs in Addison, TX

Junior Machine Learning Engineer information

See Addison, TX salary details

$32.4K

$69.5K

$106K

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

As of Sep 7, 2026, the average yearly pay for junior machine learning engineer in Addison, TX is $69,506.00, according to ZipRecruiter salary data. Most workers in this role earn between $47,000.00 and $77,400.00 per year, depending on experience, location, and employer.

What does a junior machine learning engineer do?

As a junior machine learning engineer, you work in AI, performing research with algorithms and data modeling techniques. Machine learning involves using large collections of data to create systems that are capable of making predictions, and in this field, your duties and responsibilities revolve around using advanced mathematics to design applications for use in everything from stock trading to sports betting. Some machine learning efforts involve images, and this branch of the field is known as computer vision, while other techniques which focus on text are called natural language processing (NLP). Given these divisions, titles in machine learning include computer vision engineer, NLP scientist, or simply research scientist.

What kinds of projects and responsibilities can a junior machine learning engineer expect in their first year on the job?

As a Junior Machine Learning Engineer, you’ll typically work on tasks such as data preprocessing, building and testing simple models, and supporting more senior engineers in deploying machine learning solutions. Your responsibilities may also include cleaning datasets, implementing basic algorithms, and running experiments to evaluate model performance. You’ll often collaborate closely with data scientists, software engineers, and product teams to understand project goals and learn best practices. The role provides excellent opportunities to develop your technical skills, gain exposure to various stages of the ML pipeline, and gradually take on more complex projects as you grow.

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

To succeed as a Junior Machine Learning Engineer, you need a solid grasp of programming (especially Python), foundational knowledge of algorithms and statistics, and a relevant degree in computer science, mathematics, or a related field. Familiarity with machine learning frameworks such as TensorFlow or PyTorch and tools like scikit-learn, as well as experience with version control systems like Git, are typically required. Strong problem-solving abilities, attention to detail, and a willingness to learn from feedback are valuable soft skills that help you adapt and grow in the field. These skills ensure you can effectively develop, test, and improve machine learning models while collaborating with more experienced engineers and contributing to team projects.

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

AspectJunior Machine Learning EngineerData Scientist
Required CredentialsBachelor's in CS, Data Science, or related; some experience with ML frameworksBachelor's or higher in CS, Statistics, or related; often advanced certifications
Work EnvironmentDeveloping and deploying ML models, coding, testingData analysis, statistical modeling, interpreting data insights
Employer & Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, tech, consulting
Search & Comparison IntentYesYes

While both roles involve working with data and machine learning, Junior Machine Learning Engineers focus on building and deploying models, often with coding and engineering skills. Data Scientists analyze data, create statistical models, and interpret insights. The roles overlap but differ mainly in their core responsibilities and skill emphasis.

How much do junior machine learning engineers make?

Junior machine learning engineers typically earn between $70,000 and $100,000 annually, depending on location, education, and industry. Entry-level roles often require knowledge of programming languages like Python and familiarity with machine learning frameworks such as TensorFlow or PyTorch.
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The top searched job categories for Junior Machine Learning Engineer jobs in Addison, TX are:

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Cities near Addison, TX with the most Junior Machine Learning Engineer job openings:

Junior Machine Learning Engineer jobs near you
Infographic showing various Junior Machine Learning Engineer job openings in Addison, TX as of August 2026, with employment types broken down into 95% Full Time, and 5% Contract. Highlights an 80% In-person, 5% Hybrid, and 15% Remote job distribution, with an average salary of $69,506 per year, or $33.4 per hour.

Junior Machine Learning Engineer

MyFunded Futures

Plano, TX • On-site

Full-time

Posted 5 days ago

Job description

The Junior ML Engineer will support the Company's data science function by developing, validating, and maintaining machine learning models and the data pipelines behind them. This is a hands-on, applied role: you will work with real data on problems that directly shape the product and the business, and you will be expected to explain what your models do and why they can be trusted.
You will partner closely with the Data Science and Analytics team and with stakeholders across the organization, translating business questions into well-defined analytical problems and presenting results in terms decision-makers can act on.
This role is ideal for someone early in their career who has already built and shipped machine learning models and who wants broader exposure across modeling, analytics, and data engineering.

Key Responsibilities
  • Develop, test, validate, and maintain machine learning models under the guidance of senior team members.
  • Build and maintain data pipelines and analytical datasets on the Company's cloud data platform.
  • Evaluate model performance rigorously and document assumptions, methods, and limitations.
  • Support statistical analysis, forecasting, and experimentation to inform business decisions.
  • Present technical findings clearly to non-technical audiences.
  • Contribute to standards for model documentation, validation, and monitoring.
Qualifications
  • Bachelor's degree (or equivalent) in computer science, mathematics, engineering, or a related field, with coursework in machine learning or statistical learning. Graduate degree is a plus.
  • Strong Python and PySpark skills, with the ability to write clean, tested, maintainable code.
  • Hands-on experience with a cloud data platform (Databricks, Snowflake, Fabric, or similar)
  • Strong SQL, including window functions and multi-table joins.
  • Solid understanding of core ML concepts: cross-validation, overfitting, class imbalance, data leakage (including in time-ordered data), and choosing evaluation metrics appropriate to the problem.
  • Hands-on experience with: 
    • Gradient-boosted trees (XGBoost, LightGBM)
    • Logistic regression, support vector machines, k-nearest neighbors
    • Clustering methods (k-means and others)
  • Experience with some of the following: survival / time-to-event analysis, experiment design and causal inference, simulation and Monte Carlo methods, probability calibration, Bayesian or hierarchical modeling, model monitoring and drift detection
  • Experience taking a model from development into a scheduled or production environment
  • Docker, CI/CD, and workflow orchestration experience
  • Ability to explain model behavior, including feature importance, calibration, and limitations.
  • Ability to gather and present technical results to a non-technical audience.
  • Proven experience as a machine learning engineer or in a similar role is a plus.
  • Fintech, trading, or financial services background is a plus.

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