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Junior Machine Learning Jobs in Frisco, TX (NOW HIRING)

Cotality

Senior Machine Learning Scientist

Dallas, TX · On-site

Mentor junior data scientists and machine learning engineers, providing technical guidance and driving knowledge sharing across the team. * Documentation: Create and maintain rigorous documentation ...

builders

IT Internship - Irving, TX (Summer 2027)

Irving, TX · On-site

$14.50 - $19.25/hr

Preferred junior or senior standing; other class levels will be considered. PREFERRED MAJORS/DEGREES * Computer Science * Data Science * Artificial Intelligence / Machine Learning * Software ...

AAA Auto Club Group

Data Scientist Lead

Coppell, TX · On-site

You will apply statistical methods, hypothesis testing, causal inference, machine learning, and ... As a senior contributor, you will lead projects, mentor junior data scientists, and help guide ...

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All JobsMachine Learning JobsJunior Machine Learning JobsJunior Machine Learning Jobs in Frisco, TX

Junior Machine Learning information

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How much do junior machine learning jobs pay per hour?

As of Sep 4, 2026, the average hourly pay for junior machine learning in Frisco, TX is $25.23, according to ZipRecruiter salary data. Most workers in this role earn between $15.29 and $31.06 per hour, depending on experience, location, and employer.

What does a junior machine learning engineer do?

A Junior Machine Learning Engineer assists in the development and implementation of machine learning models and algorithms under the supervision of more experienced engineers. They typically help with data collection, cleaning, feature engineering, model training, and evaluation. Junior engineers may also write code, test prototypes, and contribute to improving model performance while learning best practices in the field. Their role often involves collaborating with data scientists and software engineers to integrate machine learning solutions into products or services.

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

To thrive as a Junior Machine Learning Engineer, you need a solid understanding of programming (especially Python), basic statistics, linear algebra, and familiarity with machine learning concepts, typically supported by a relevant degree or coursework. Proficiency in tools and frameworks like scikit-learn, TensorFlow, PyTorch, and version control systems such as Git is often expected. Strong problem-solving abilities, curiosity, and effective communication are crucial soft skills for collaborating with teams and explaining technical concepts. These skills and qualities are important because they enable you to contribute effectively to building, testing, and improving machine learning models in real-world applications.

What types of projects and tasks can a junior machine learning professional typically expect to work on in their first year?

As a Junior Machine Learning professional, you’ll often support senior data scientists and engineers by preparing data, implementing basic algorithms, and assisting with model evaluation. Your daily tasks may include data cleaning, feature engineering, running experiments, and writing code to automate data pipelines. You might also help document processes and present your findings to team members. While the work is often collaborative, you’ll have opportunities to take ownership of smaller projects and progressively contribute to larger initiatives as you gain experience.

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

AspectJunior Machine LearningData Scientist
Required CredentialsBachelor's in CS, Data Science, or related field; some experience with ML toolsBachelor's or Master's in CS, Statistics, or related; strong programming and statistical skills
Work EnvironmentEntry-level projects, supervised tasks, team collaborationAdvanced analysis, model development, cross-functional teams
Industry UsageCommon in tech companies, startups, research labsWidespread across industries like finance, healthcare, tech

Junior Machine Learning roles focus on foundational ML tasks and learning on the job, while Data Scientists handle complex data analysis, model building, and strategic insights. The roles differ mainly in experience level and scope of responsibilities, but both require strong technical skills and familiarity with data tools.

What are the most commonly searched types of Machine Learning jobs in Frisco, TX?

The most popular types of Machine Learning jobs in Frisco, TX are:

What are popular job titles related to Junior Machine Learning jobs in Frisco, TX?

For Junior Machine Learning jobs in Frisco, TX, the most frequently searched job titles are:

What job categories do people searching Junior Machine Learning jobs in Frisco, TX look for?

The top searched job categories for Junior Machine Learning jobs in Frisco, TX are:

What cities near Frisco, TX are hiring for Junior Machine Learning jobs?

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

Junior Machine Learning jobs near you
Infographic showing various Junior Machine Learning job openings in Frisco, TX as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 21% Part Time, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $52,476 per year, or $25.2 per hour.

Junior Machine Learning Engineer

MyFunded Futures

Plano, TX • On-site

Full-time

Posted 3 days ago

New

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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