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Entry Level Machine Learning Engineer Jobs in Frisco, TX

Senior ML Engineer

Addison, TX · On-site

$101.20K - $138.90K/yr

Develop machine learning models and algorithms to address business needs. Collaborate with data scientists and software engineers to design and implement scalable and efficient solutions. Clean ...

Senior ML Engineer

Addison, TX · On-site

$101.20K - $138.90K/yr

Responsibilities: • Develop machine learning models and algorithms to address business needs. • Collaborate with data scientists and software engineers to design and implement scalable and ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

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Entry Level Machine Learning Engineer information

See Frisco, TX salary details

$28.1K

$64.9K

$110.4K

How much do entry level machine learning engineer jobs pay per year?

As of May 29, 2026, the average yearly pay for entry level machine learning engineer in Frisco, TX is $64,918.00, according to ZipRecruiter salary data. Most workers in this role earn between $48,200.00 and $73,500.00 per year, depending on experience, location, and employer.

What is an Entry Level Machine Learning Engineer job?

An Entry Level Machine Learning Engineer is responsible for developing, testing, and deploying machine learning models under the guidance of senior engineers. They work with datasets, implement algorithms, and optimize model performance. Their role often involves data preprocessing, feature engineering, and collaborating with data scientists and software engineers. Strong programming skills in Python, knowledge of ML frameworks like TensorFlow or PyTorch, and an understanding of statistics and algorithms are essential. This position serves as a foundation for building expertise in artificial intelligence and data-driven decision-making.

What are the key skills and qualifications needed to thrive in the Entry Level Machine Learning Engineer position, and why are they important?

To thrive as an Entry Level Machine Learning Engineer, you need a solid understanding of machine learning algorithms, programming languages like Python, and a degree in computer science, engineering, or a related field. Familiarity with tools such as TensorFlow, PyTorch, scikit-learn, and version control systems like Git is highly valuable, and completing online courses or certifications can further demonstrate your skills. Strong analytical thinking, attention to detail, and effective communication are important soft skills in this role. These abilities are essential because they enable you to build accurate models, work collaboratively with teams, and communicate insights to stakeholders.

What are some typical projects or tasks an Entry Level Machine Learning Engineer might work on?

As an Entry Level Machine Learning Engineer, you’ll often work on tasks such as data preprocessing, feature engineering, and assisting in training and evaluating models under the guidance of senior engineers or data scientists. You may help develop prototypes, automate data collection pipelines, and collaborate with software engineers to integrate machine learning solutions into products. Working in this role typically involves frequent collaboration in a team environment, participating in code reviews, and learning best practices for scalable model deployment. These foundational experiences are designed to build your technical expertise and set the stage for future growth within the field.
What are the most commonly searched types of Machine Learning Engineer jobs in Frisco, TX? The most popular types of Machine Learning Engineer jobs in Frisco, TX are:
What are popular job titles related to Entry Level Machine Learning Engineer jobs in Frisco, TX? For Entry Level Machine Learning Engineer jobs in Frisco, TX, the most frequently searched job titles are:
What job categories do people searching Entry Level Machine Learning Engineer jobs in Frisco, TX look for? The top searched job categories for Entry Level Machine Learning Engineer jobs in Frisco, TX are:
What cities near Frisco, TX are hiring for Entry Level Machine Learning Engineer jobs? Cities near Frisco, TX with the most Entry Level Machine Learning Engineer job openings:
Infographic showing various Entry Level Machine Learning Engineer job openings in Frisco, TX as of May 2026, with employment types broken down into 1% Internship, 91% Full Time, 5% Part Time, 2% Contract, and 1% Nights. Highlights an 78% Physical, 3% Hybrid, and 19% Remote job distribution, with an average salary of $64,918 per year, or $31.2 per hour.
Machine Learning Engineer, Unity Catalog

Machine Learning Engineer, Unity Catalog

Glow Networks

Dallas, TX • On-site

Full-time

Posted 9 days ago


Job description

Job Description: Machine Learning Engineer, Unity Catalog
Location: Remote (PST)
Duration: 10 Months
We are looking for an ML engineer with expertise in Unity Catalog and Feature Store in Databricks to help us build and maintain a solid foundation for our data and machine learning workflows. You will work on organizing data, managing access, and enabling machine learning models to operate efficiently in production
• Proficiency with Java
The ML Engineers will be supporting 3 web services applications - tech stack - Java 11 - Azure, AKS, and APIM.
• Set up and manage Unity Catalog in Databricks to organize and secure data access across teams
• Design and operationalize Feature Stores to support machine learning models in production
• Build efficient data pipelines to process and serve features to ML workflows
• Collaborate with teams using Databricks, Azure Cosmos DB, and other Azure tools to integrate data solutions
• Monitor and optimize the performance of pipelines and feature stores
• 5 - Strong experience with Unity Catalog in Databricks for managing data assets and access control
• 4 - Hands-on experience working with Databricks Feature Store or similar solutions
• 2 - Knowledge of building and maintaining scalable ETL pipelines in Databricks
• 2- Familiarity with Azure tools like Azure Cosmos DB and ACR
• 2- Understanding of machine learning workflows and how feature stores fit into the pipeline
• 5- Strong problem-solving skills and a collaborative mindset
• 3- Proficiency in Python and Spark for data engineering tasks
• 3- Experience with monitoring tools like Splunk or Datadog to ensure system reliability
• 2- Familiarity with AKS for deploying and managing containers