Daily Workflow: A Day in the Life
Unlike classroom assignments where you are given nice, clean, pre-packaged datasets, your production work as an intern is chaotic. As an AI/ML online intern, you will typically work on three primary tasks.
1. The Foundation: Dataset Cleaning and Preprocessing
You may have heard the industry phrase, “Garbage in, garbage out”. In a machine learning pipeline, this is a fact of life. As a machine learning intern you will most likely spend 60-70% of your time cleaning data before it is sent to the model.
Your daily tasks may include:
- Handling missing values: you will either drop the rows with missing data or use an imputation method to fill them in (mean, median, or KNN imputation).
- Data Normalization & Scaling: the numeric variables must be brought onto a similar scale to ensure thatgradient descent machine learning algorithms will converge efficiently.
- Encoding categorical features: typically text inputs need to be encoded numerically in order for the model to use it (One-Hot Encoding, Label Encoding).
- Outlier detection: you may be responsible for identifying and potentially removing an outliers that could affect your model’s predictive capability.
2. The Core Task: Model Training and Tuning
Once the data pipeline is stable you may transition to the model itself. The reality of enterprise ML work is that you will not typically build models from scratch but rather tune pre-built architectures to suit a specific business case.
Your tasks here may include:
- Hyperparameter tuning: through techniques like GridSearch and RandomSearch, you will find a perfect combination of hyperparameter values that balance a model and reduce the chance of overfitting.
- Using Pre-trained models: through the use of transfer learning, you will utilize cutting-edge models like the models like the ResNet architectures for computer vision and the BERT or GPT model variations for NLP and finetune on the company’s specific, proprietary data.
- Tracking experiments: you will likely be training multiple model variations at once to see what version performs best on metrics such as accuracy, precision, recall, and F1 score.
3. The Communication Loop: Virtual Standups & Collaboration
An AI/ML online internship requires that you communicate actively and often to be visible in your team. Most remote engineering companies follow the Agile method, thus your days will usually begin or end with a quick virtual standup meeting.
During standups, you will report on:
- Tasks that were completed the previous day
- What you are currently working on
- Any issues that you are experiencing “blockers”
The Tech Stack: What you can expect to use every day
You can expect to utilize a certain toolkit in your machine learning internship. While you do not have to be an absolute pro in day one, understanding the purpose of each of these tools can provide you a leg up.
| Category | Standard Industry Tools | What You Use It For |
| Programming Language | Python | The universal language for AI/ML development, script writing, and automation. |
| Data Manipulation | Pandas, NumPy | Loading large CSV/JSON files, filtering rows, and handling matrix operations. |
| Machine Learning Frameworks | Scikit-Learn, TensorFlow, PyTorch | Building classical regression/classification models and deep learning architectures. |
| Version Control & Collaboration | Git, GitHub, GitLab | Managing code versions, submitting Pull Requests, and conducting peer code reviews. |
| Development Environments | Jupyter Notebooks, VS Code, Google Colab | Writing code, documenting experiments, and utilizing remote GPU acceleration. |
| Experiment Tracking | MLflow, Weights & Biases | Logging model metrics, parameters, and loss curves over multiple training runs. |
The Argument for a Remote AI/ML Internship
Being offered a remote AI and ML internship goes above and beyond having an impressive internship title; it will truly revolutionize the way you approach software engineering.
- You’ll get exposure to scale: Gone are the days of training tiny models on toy datasets in a local environment; you’ll learn how to effectively work with distributed systems and massive data lakes hosted remotely.
- You’ll hone independent problem-solving skills: With distance from your mentor and team, you will inevitably become increasingly resourceful. When a problem inevitably arises in your code,
- you’ll have no choice but to become adept at using documentation, blogs, and Stack Overflow to solve your problem before even considering contacting your mentor.
- You’ll gain essential experience to thrive in today’s workforce: By working with tools such as Slack, Jira, and Zoom, you’ll gain a solid understanding of how distributed, globally dispersed teams work efficiently.s collaborate globally.
Despite the learning curve during your first few weeks, if you are diligent with your data cleaning and aim to consistently maintain an organized experimental structure, you’ll be an incredibly valuable addition to any distributed engineering team.