Machine Learning Engineer Intern – Machine Learning

Job Description

Machine Learning Engineer Intern | Jobs via Dice

The Tone:
This is an internship at Aivra Health LLC, located in the United States. Aivra Health is seeking a Machine Learning Engineer Intern to join its AI/ML team. This role is crucial for developing and deploying scalable machine learning models to solve real-world business challenges. The intern will contribute directly to the production environment by building, training, evaluating, and deploying ML models.

The TL;DR
• Role: Internship
• Type: Full-Time (40 hours/week)
• Location: United States
• Pay: $28–$45 hourly
• Visa Sponsorship: H1B available; welcomes STEM OPT, OPT & CPT candidates
• Team: AI/ML team, works closely with Data Scientists and Software Engineers
• Mission: Develop, train, evaluate, and deploy machine learning models that solve real-world business problems
• Tech Stack: Python, R, Java, SQL, Scikit-learn, TensorFlow, Keras, PyTorch, XGBoost, LightGBM, Pandas, NumPy, Apache Spark, PySpark, Hadoop, Natural Language Processing (NLP), Computer Vision, Deep Learning, Transformers, LLM fundamentals, AWS (SageMaker, S3, EC2), Microsoft Azure ML, Google Cloud AI Platform, Docker, Kubernetes, MLflow, CI/CD pipelines, Model Deployment & Monitoring, Git, REST APIs, Feature Engineering, Model Evaluation Metrics, A/B Testing, Agile/Scrum

What You’ll Actually Do
• Model Development: Build and train machine learning and deep learning models.
• Data Preparation: Perform data preprocessing, feature engineering, and exploratory data analysis.
• Model Optimization: Implement supervised and unsupervised learning algorithms, and optimize model performance through hyperparameter tuning.
• Deployment & Monitoring: Deploy machine learning models using REST APIs or cloud services, and work on their monitoring and validation.
• Collaboration & Documentation: Collaborate with cross-functional teams in an Agile/Scrum environment and document ML workflows.

The Must-Haves
• Background: Currently pursuing or recently completed a Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, or a related STEM field, with a strong understanding of Machine Learning fundamentals, Probability, Statistics, Linear Algebra, and basic Data Structures and Algorithms.
• Experience: Prior machine learning internship or academic research experience, with a focus on building and deploying models.
• Skills: Proficiency in Python, experience with ML frameworks like Scikit-learn, TensorFlow, Keras, PyTorch, and strong skills in data processing tools such as Pandas and NumPy. Knowledge of cloud platforms (AWS, Azure ML, Google Cloud AI Platform) and version control with Git is also required.
• Bonus: Knowledge of MLOps practices, strong problem-solving and analytical skills, and good communication and teamwork abilities.