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

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

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 ...

MS in Machine Learning, Mathematics, Statistics, Computer Science, or a related field * 5+ years of experience in machine learning engineering or related field * 3+ years of experience as a manager

New

ML Engineer

Dallas, TX · On-site +1

Machine Learning Engineer (Llama AI Platform) Location: Remote (Preferred U.S. Time Zones) Employment Type: Full-Time Company: Performacentric About Performacentric Performacentric helps small and ...

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

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 ...

Senior ML Engineer

Addison, TX · On-site

$101K - $138K/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 ...

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Machine Learning Engineer information

See Rowlett, TX salary details

$28.3K

$115.5K

$173.6K

How much do machine learning engineer jobs pay per year?

As of Jun 13, 2026, the average yearly pay for machine learning engineer in Rowlett, TX is $115,535.00, according to ZipRecruiter salary data. Most workers in this role earn between $91,100.00 and $139,100.00 per year, depending on experience, location, and employer.

Is ML full of coding?

Machine Learning Engineers typically do a significant amount of coding, especially in languages like Python or R, to develop algorithms, preprocess data, and build models. Strong programming skills are essential, along with knowledge of frameworks such as TensorFlow or PyTorch, but the role also involves data analysis, model evaluation, and collaboration with teams. Coding is a core component of the job, though some tasks may involve model deployment and optimization that require different skills.

What engineers make $500,000?

Senior machine learning engineers with extensive experience, advanced skills in deep learning and data science, and often working in high-paying industries such as finance or technology can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially at large tech companies or startups with significant funding.

What do machine learning engineers do?

Machine learning engineers develop algorithms and models that enable computers to learn from data and make predictions or decisions. They often work with large datasets, use programming languages like Python or Java, and utilize tools such as TensorFlow or PyTorch to build, test, and deploy machine learning systems in production environments.

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.

Which 5 jobs will survive AI?

Machine Learning Engineers are likely to continue to be in demand as they develop, implement, and maintain AI systems, requiring specialized skills in programming, data analysis, and model optimization. Roles that involve complex problem-solving, creativity, and human interaction—such as healthcare professionals, educators, skilled tradespeople, and certain managerial positions—are also expected to persist despite AI advancements. These jobs typically require emotional intelligence, adaptability, and domain expertise that AI cannot easily replicate.

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 are the most commonly searched types of Machine Learning Engineer jobs in Rowlett, TX? The most popular types of Machine Learning Engineer jobs in Rowlett, TX are:
What are popular job titles related to Machine Learning Engineer jobs in Rowlett, TX? For Machine Learning Engineer jobs in Rowlett, TX, the most frequently searched job titles are:
What cities near Rowlett, TX are hiring for Machine Learning Engineer jobs? Cities near Rowlett, TX with the most Machine Learning Engineer job openings:
Infographic showing various Machine Learning Engineer job openings in Rowlett, TX as of June 2026, with employment types broken down into 95% Full Time, 3% Part Time, 1% Contract, and 1% Nights. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $115,535 per year, or $55.5 per hour.
Compliance, Machine Learning Engineer, Dallas, Vice President

Compliance, Machine Learning Engineer, Dallas, Vice President

Goldman Sachs

Dallas, TX • On-site

Other

Posted 25 days ago


Goldman Sachs rating

8.3

Company rating: 8.3 out of 10

Based on 25 frontline employees who took The Breakroom Quiz

29th of 141 rated banks


Job description

Are you passionate about delivering mission-critical, high quality machine learning models, using cutting-edge technology, in a dynamic environment? 

OUR IMPACT

We are Compliance Engineering, a global team of more than 300 engineers and scientists who work on the most complex, mission-critical problems. 

We:

  • build and operate a suite of platforms and applications that prevent, detect, and mitigate regulatory and reputational risk across the firm. 
  • have access to the latest technology and to massive amounts of structured and unstructured data.
  • leverage modern frameworks to build responsive and intuitive UX/UI and Big Data applications.

Within Compliance engineering, we are hiring for a Machine Learning Engineering role within Models Engineering. The firm is making a significant investment improve the precision/ recall of the Compliance models portfolio in 2024. To achieve that we are hiring experienced MLEs who have experience of developing and deploying ML models for big data in a distributed architecture.

HOW YOU WILL FULFILL YOUR POTENTIAL

As a member of our team, you will:

  • Work with large scale structure and unstructured data. Drive end to end Machine Learning projects that have a high degree of scale and complexity
  • Build infra for machine learning which involves feature engineering and scaling models to work at scale
  • Develop, productionize, and maintain ml models
  • Run ML experiments by constantly tuning the features and the modeling approaches, documenting findings and results
  • Collaborate closely with ML researchers, to accelerate the usage of cutting edge models
  • Perform code reviews and ensure code quality

QUALIFICATIONS

A successful candidate will possess the following attributes:

  • A Bachelor's or Master's degree in Computer Science, or a similar field of study.
  • 10+ years of hands-on experience with building scalable machine learning systems 
  • Solid coding skills and strong Computer Science fundamentals (algorithms, data structures, software design)
  • Expertise in Python & PySpark
  • Experience in working with distributed technologies like Scala, Pyspark, Iceberg, HDFS file formats (avro, parquet), AWS/ GCP,  big data feature engineering.
  • Experience in system design and evaluating the pros and cons of database choices, schema definition for data storage.
  • Extensive experience with Machine Learning and Deep Learning toolkits (Tensorflow, PyTorch, Scikit-Learn, HuggingFace)

Experience in some of the following is desired and can set you apart from other candidates : 

  • Prior experience with LLMs and Prompt Engineering
  • Prior experience in architecting/ deploying ML applications on AWS/ GCP
  • Prior experience in code reviews/ architecture design for distributed systems. 
ABOUT GOLDMAN SACHS

 
At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world. 

 
We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. Learn more about our culture, benefits, and people at GS.com/careers. 

 
We're committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: https://www.goldmansachs.com/careers/footer/disability-statement.html

 

 
The Goldman Sachs Group, Inc., 2023. All rights reserved.
Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veterans status, disability, or any other characteristic protected by applicable law.

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Goldman Sachs logo

About Goldman Sachs

Sourced by ZipRecruiter

At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world. We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs.

Industry

Finance and insurance

Company size

10,000+ Employees

Headquarters location

New York, NY, US

Year founded

1869