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Machine Learning Engineer Jobs in Dallas, TX (NOW HIRING)

Veho

Senior Machine Learning Platform Engineer

Dallas, TX · On-site

$103K - $142K/yr

The Senior Machine Learning Platform Engineer will work alongside data scientists and software engineers to create and maintain ML infrastructure, ensuring the deployment and performance of models in ...

Omni Inclusive

Machine Learning

Dallas, TX

Machine Learning - Decision Trees, Random Forests, Rule Mining, Clustering, PCA, Support Vector ... Programming & Scripting - Python, R, Unix-Shell scripting, PySpark

Cisco

Principal Machine Learning Engineer

Dallas, TX

$291K - $369K/yr

Partner with executive leadership, engineering, product, and data science teams to ensure AI ... Solid proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow) * Experience ...

ClifyX

AI/ML Engineer

Dallas, TX

AI/Client Engineer Further requirements and responsibilities are as follows ... Design Enterprise Machine Learning platforms that are capable of running predictive models.

Resolve Tech Solutions

Senior ML Engineer

Addison, TX · On-site

$101K - $138K/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 ...

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All JobsMachine Learning Engineer JobsMachine Learning Engineer Jobs in Dallas, TX

Machine Learning Engineer information

See Dallas, TX salary details

$31.2K

$127.4K

$191.4K

How much do machine learning engineer jobs pay per year?

As of Jun 9, 2026, the average yearly pay for machine learning engineer in Dallas, TX is $127,360.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,400.00 and $153,300.00 per year, depending on experience, location, and employer.

What are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

What Does a Machine Learning Engineer Do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What are some common challenges faced by Machine Learning Engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

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

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

What jobs make $3,000 a month without a degree?

A Machine Learning Engineer typically requires a degree, but roles such as data annotator, technical support specialist, or freelance programmer can sometimes earn around $3,000 monthly without a formal degree, especially with relevant skills and experience. These jobs often involve self-taught skills, online certifications, or on-the-job training and may require proficiency in tools like Python or cloud platforms.
More about Machine Learning Engineer jobs
What are the most commonly searched types of Machine Learning Engineer jobs in Dallas, TX? The most popular types of Machine Learning Engineer jobs in Dallas, TX are:
What are popular job titles related to Machine Learning Engineer jobs in Dallas, TX? For Machine Learning Engineer jobs in Dallas, TX, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer jobs in Dallas, TX look for? The top searched job categories for Machine Learning Engineer jobs in Dallas, TX are:
What cities near Dallas, TX are hiring for Machine Learning Engineer jobs? Cities near Dallas, TX with the most Machine Learning Engineer job openings:
Machine Learning Engineer jobs near you
Infographic showing various Machine Learning Engineer job openings in Dallas, TX as of May 2026, with employment types broken down into 1% Internship, 54% Full Time, 43% Part Time, and 2% Contract. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $127,360 per year, or $61.2 per hour.
Senior Machine Learning Platform Engineer

Senior Machine Learning Platform Engineer

Veho

Dallas, TX • On-site

$103K - $142K/yr

Full-time

Posted 26 days ago

Job description

Job Summary:
Veho is a company focused on improving logistics and user experiences through advanced machine learning. The Senior Machine Learning Platform Engineer will work alongside data scientists and software engineers to create and maintain ML infrastructure, ensuring the deployment and performance of models in production.
Responsibilities:
• Build reliable, efficient, and scalable infrastructure for our AI/ML capabilities
• Create robust data pipelines to feed analyses and models
• Enable forecasting, network orchestration, and live pricing systems
• Ensure data quality and data integrity through best practices in data integration
• Build out robust feature stores, model orchestration tooling, experimentation tooling, model performance monitoring.
• Create standards and templates for model development and deployment across all Data Science teams.
Qualifications:
Required:
• Bachelor’s Degree plus at least 3 years of experience in machine learning engineering, or Master’s Degree plus at least 2 years in machine learning engineering
• Developing and optimizing MLOps pipelines for speed, reliability, and observability
• Utilizing statistical modeling or machine learning techniques to solve business problems
• Strong proficiency in Python and SQL
• Hands-on experience with open-source languages and tooling for large-scale ML (e.g., Ray, Flink, Feast)
• Working with Data Warehouses (e.g., Redshift, Databricks, Snowflake)
• Utilizing cloud-based (AWS Preferred) data engineering and data science tools
Preferred:
• Experience building ML systems in Startups is a plus
• Experience with DS/ML in Logistics/Supply Chain is a plus
Company:
Reinventing delivery and returns for the e-commerce era. Founded in 2016, the company is headquartered in Boulder, USA, with a team of 501-1000 employees. The company is currently Late Stage.
Veho logo

About Veho

Sourced by ZipRecruiter

Veho's mission is to revolutionize the world of package delivery by creating exceptional experiences for customers and drivers. For too long, parcel delivery companies have focused solely on efficiencies and cost-management. Veho focuses on the end-customer. As the first technology company of its kind, Veho replaces the old delivery trucks with a platform of crowdsourced drivers and a network of hyper-local delivery stations. We partner with some of the most recognized consumer brands such as Warby Parker and Hello Fresh to provide an incredible experience with every delivery, and give customers control on how, when and where their package is delivered. Customers LOVE us. Despite growing at a record-speed over the past two years, we maintain an incredibly high customer satisfaction rating of 4.9/5 stars, and an unprecedented on-time delivery rate of 99.9% - far above every other company in the country. In short, we are building the logistics platform of the future. Veho is backed by former top executives and board members at Uber, FedEx, UPS, eBay and Amazon, three former public company CEOs, early investors in Lyft and Instacart, as well as prominent venture capital firms General Catalyst, Tiger Global, and Softbank. We are a team of leaders who are passionate about building an incredible company that will change the face of this industry.

Company size

1,001 - 5,000 Employees

Headquarters location

CO, US

Year founded

2016

Social media

Twitter

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