Role overview
- You should be pursuing a relevant degree, have strong math/statistics skills, Python proficiency, ML knowledge, and experience with Git, Unix, big data, cloud platforms, MLOps, and NLP.
- You will design and implement ML algorithms, evaluate model performance, fine-tune models with TensorFlow/PyTorch, assist in generative AI development, and document technical specifications.
- Design and implement machine learning algorithms for various applications, including classification, regression, and clustering problems
- Conduct thorough model evaluation, validation, and performance optimization using industry-standard metrics
- Implement and fine-tune various ML models using frameworks such as TensorFlow, PyTorch, and Scikit-learn
- Assist in the development of generative AI applications using state-of-the-art models
- Document model architectures, experimental results, and technical specifications
Responsibilities
- Design and implement machine learning algorithms for various applications, including classification, regression, and clustering problems
- Conduct thorough model evaluation, validation, and performance optimization using industry-standard metrics
- Implement and fine-tune various ML models using frameworks such as TensorFlow, PyTorch, and Scikit-learn
- Assist in the development of generative AI applications using state-of-the-art models
- Document model architectures, experimental results, and technical specifications
Basic qualifications
- Currently pursuing a Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, or a related technical field. Preference will be given to students with prior work experience
- Strong foundation in mathematics and statistics, including:
- Linear algebra and calculus
- Probability theory and statistical modeling
- Optimization techniques
- Proficiency in Python programming with experience in ML libraries and frameworks
- Understanding of fundamental machine learning concepts:
- Supervised and unsupervised learning algorithms
- Model evaluation and validation techniques
- Feature engineering and selection methods
- Experience with version control systems (Git) and data processing tools
- Experience with Unix-based OS
- Able to work for the complete summer break (May - August or June - September)
Preferred qualifications
- Previous coursework or projects in machine learning, deep learning, or AI
- Experience with big data technologies such as Apache Spark
- Familiarity with cloud platforms (AWS, Google Cloud) and their ML services
- Knowledge of MLOps practices and model deployment workflows
- Understanding of deep learning architectures and their applications
- Experience with Natural Language Processing (NLP) concepts and techniques
- Remote-based position located in the United States or Canada.
- Email us at calix.interview@calix.com ; or
- Call us at +1 (408) 514-3000.
Tags & focus areas
Used for matching and alerts on DevFound Ai Intern Entry Level Machine Learning Remote Aws Tensorflow Pytorch Scikit Learn Python