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Data Science & Artificial Intelligence · Verified Opening #79

Machine Learning Engineer

business anthropic location_on Dallas, TX, USA (Hybrid) apartment Hybrid Full Time

Compensation

$85K – 115K/yr

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Published Date

1 week ago

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Work Arrangement

Hybrid • Dallas, TX, USA

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Open Positions

2 openings

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Experience Level

Entry-Level to Mid-Level

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About the opportunity

Join American Tech Global LLC as a Machine Learning Engineer and build intelligent, data-driven solutions using Python, machine learning algorithms, and modern AI technologies. This opportunity is suitable for candidates with Data Science and AI training, hands-on project experience, and a passion for solving real-world business problems.

American Tech Global LLC is seeking a motivated Machine Learning Engineer to contribute to the development, testing, and deployment of intelligent applications. In this role, you will collaborate with data scientists, software engineers, and product teams to transform raw data into predictive models and practical AI solutions. You will work with Python, SQL, machine learning frameworks, and data visualization tools while contributing to the complete machine learning lifecycle, from data preparation and exploratory analysis to model evaluation and deployment. Candidates who have completed a Data Science and AI program and can demonstrate their abilities through practical projects, internships, or relevant professional experience are encouraged to apply. This position offers opportunities to strengthen engineering skills, contribute to production-oriented AI projects, and grow into advanced machine learning and AI engineering roles. The stated location and compensation are illustrative hiring-post details and should be confirmed by the employer before publication.

task Core Responsibilities

What you will do

check_circle Develop, train, evaluate, and optimize machine learning models for classification, regression, forecasting, and predictive analytics use cases.
check_circle Use Python libraries such as Pandas, NumPy, Scikit-learn, and TensorFlow or PyTorch to build and test AI solutions.
check_circle Collect, clean, transform, and analyze structured and unstructured datasets using Python and SQL.
check_circle Perform feature engineering, exploratory data analysis, model validation, and performance optimization to improve prediction quality.
check_circle Collaborate with data scientists, software engineers, and product stakeholders to translate business requirements into machine learning solutions.
check_circle Build reproducible model training workflows and assist with deploying, monitoring, and maintaining machine learning models in development or production environments.
check_circle Document experiments, communicate findings, and recommend data-driven improvements to technical and business teams.

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What we are looking for

arrow_circle_right Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field, or equivalent practical experience.
arrow_circle_right Knowledge of Python programming, data structures, data manipulation, and SQL fundamentals.
arrow_circle_right Understanding of supervised and unsupervised learning, regression, classification, clustering, and model evaluation techniques.
arrow_circle_right Hands-on experience with machine learning projects through academic work, internships, personal projects, or professional experience.
arrow_circle_right Familiarity with Git, Jupyter Notebook, data visualization, and common machine learning development workflows.
arrow_circle_right Strong analytical thinking, problem-solving, communication skills, and willingness to learn new AI technologies.

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Skills & Tech Stack

Machine Learning Python Data Science SQL Scikit-learn TensorFlow PyTorch Artificial Intelligence

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Why candidate applications stand out

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Verified Technical Credentials

Applications include direct proof-of-work repositories and instructor verification endorsements.

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Cover Note

anthropic

Dallas, TX, USA (Hybrid)

Verified enterprise hiring partner. Hiring directly through verified technical pipelines and accelerated talent networks.

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