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# Junior Machine Learning Engineer

- **Company:** Ova Technologies
- **Location:** New York, NY, United States
- **Experience:** Starter
- **Contract:** Internship / Graduate position
- **Skills:** Clean Code Principles, Artificial Intelligence, Amazon Web Services, Data Analysis, Computer Vision, Microsoft Azure, Business Software, Cloud Computing, Extract Transform Load (ETL), Data Structures, Data Visualization, Relational Databases, Linux, Github, Python (Programming Language), Machine Learning, NumPy, Object-Oriented Software Development, Open Source Technology, Performance Tuning, Software Tools, Tensorflow, Standard Sql, Azure Machine Learning, Software Engineering, SQL Databases, Unstructured Data, Jupyter Notebook, Google Cloud, Feature Engineering, Pytorch, Large Language Models, Model Validation, Generative AI, Git, Pandas, Matplotlib, Scikit Learn, Information Technology, Plotly, Machine Learning Operations, Restful APIs, Data Pipelines, Software Library, Docker, Unsupervised Learning, Microservices
- **Published:** November 4, 2026
- **Apply:** https://www.thejobnetwork.com/job/9666664b-1b23-41e1-98e9-b08baab6b35e/junior-machine-learning-engineer

## About the Role

* Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Information Technology, Statistics, Mathematics, or a related field.
 * 0 2 years of experience in machine learning, data science, or software development (internships and academic projects are welcome).
 * Strong understanding of machine learning fundamentals, statistics, and data analysis.
 * Proficiency in Python programming.
 * Experience with machine learning libraries such as Scikit-learn, PyTorch, or TensorFlow.
 * Knowledge of data structures, algorithms, and object-oriented programming.
 * Familiarity with SQL and relational databases.
 * Experience using Git for version control.

Preferred Qualifications

 * Exposure to Natural Language Processing (NLP), Computer Vision, or Generative AI.
 * Familiarity with Large Language Models (LLMs) and AI application development.
 * Knowledge of data visualization tools such as Matplotlib, Seaborn, or Plotly.
 * Basic understanding of cloud platforms (AWS, Azure, or Google Cloud).
 * Experience with Docker and Linux.
 * Academic, internship, or personal machine learning projects available on GitHub.

Technical Skills

 * Python
 * SQL
 * Scikit-learn
 * PyTorch or TensorFlow
 * Pandas
 * NumPy
 * Matplotlib
 * Seaborn
 * Git
 * Docker (basic)
 * Linux (basic)
 * Jupyter Notebook

Soft Skills

 * Strong analytical and problem-solving abilities.
 * Eagerness to learn and grow in machine learning and AI.
 * Good communication and teamwork skills.
 * Attention to detail and commitment to writing high-quality code.
 * Ability to work in a collaborative and fast-paced environment.

Nice to Have

 * Internship experience in machine learning or data science.
 * Knowledge of MLOps concepts and model deployment.
 * Familiarity with REST APIs and microservices.
 * Exposure to data engineering tools and ETL pipelines.
 * Participation in hackathons, coding competitions, or open-source AI projects.

## Description

We are looking for a passionate Junior Machine Learning Engineer to join our AI and Data Science team. This role is ideal for recent graduates or early-career professionals who are eager to build, train, and deploy machine learning models while working on real-world AI applications. You will collaborate with experienced engineers and data scientists to develop scalable machine learning solutions and contribute to the end-to-end ML lifecycle., * Assist in designing, developing, and deploying machine learning models for business applications.

 * Collect, clean, preprocess, and analyze structured and unstructured data.
 * Build and evaluate machine learning models using supervised and unsupervised learning techniques.
 * Perform feature engineering, model training, hyperparameter tuning, and performance evaluation.
 * Develop data pipelines and automate ML workflows.
 * Support model deployment, monitoring, and performance optimization in production environments.
 * Collaborate with data scientists, software engineers, and product teams to deliver AI-driven solutions.
 * Document experiments, model performance, and technical implementations.
 * Stay up to date with the latest machine learning tools, frameworks, and best practices.