We are looking for a passionate and curious Entry-Level Machine Learning Engineer & Developer to join our AI and Data team. In this role, you’ll contribute to building intelligent systems and data-driven applications by assisting in the development, training, deployment, and monitoring of machine learning models. This is a hands-on learning opportunity for someone eager to grow into a full-stack ML engineering role.
Key Responsibilities
Model Development &... Visa mer
We are looking for a passionate and curious Entry-Level Machine Learning Engineer & Developer to join our AI and Data team. In this role, you’ll contribute to building intelligent systems and data-driven applications by assisting in the development, training, deployment, and monitoring of machine learning models. This is a hands-on learning opportunity for someone eager to grow into a full-stack ML engineering role.
Key Responsibilities
Model Development & Research
Assist in developing and training ML models for tasks like prediction, classification, recommendation, or NLP.
Experiment with different algorithms using real-world datasets under the guidance of senior team members.
Perform data exploration, preprocessing, feature engineering, and visualization.
Engineering & Automation
Support the creation of end-to-end ML workflows and basic data pipelines.
Contribute to deploying ML models into production environments using APIs or automation scripts.
Work with containerized environments (e.g., Docker) and version control systems (e.g., Git).
Learning & Collaboration
Participate in code reviews, brainstorming sessions, and research discussions.
Document your experiments, learnings, and processes clearly for reproducibility and team sharing.
Learn best practices in software development, ML experimentation, and model evaluation.
Monitoring & Maintenance
Help in testing, validating, and monitoring deployed models and data pipelines.
Identify potential issues like data drift or performance degradation.
Required Qualifications
Bachelor's degree in Computer Science, Data Science, Engineering, Statistics, or a related field.
Strong programming skills in Python.
Basic understanding of machine learning concepts and algorithms (e.g., linear regression, decision trees, clustering, etc.).
Exposure to libraries like scikit-learn, pandas, NumPy, or TensorFlow/PyTorch (via academic projects, internships, or personal work).
Familiarity with basic software engineering tools: Git, Jupyter Notebooks, or simple APIs.
Preferred Qualifications
Internship or academic project experience involving machine learning or data science.
Knowledge of and cloud environments (AWS, Azure, or GCP) is a plus.
Understanding of REST APIs or experience in integrating models into applications.
Interest in MLOps concepts like model versioning, CI/CD for ML, and model monitoring.
What You’ll Gain
Mentorship from experienced engineers and data scientists.
Practical experience working on real-world ML problems.
Access to state-of-the-art tools, cloud resources, and continuous learning opportunities.
A collaborative, inclusive team environment with room for career growth.
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