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Machine Learning Engineering Intern
Bask Health. Assist with developing machine learning models and supporting engineering systems across Bask Health.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates foundational programming skills in Python and a basic understanding of machine learning concepts, including supervised learning and model evaluation. Capable of preparing datasets, developing machine learning models, and collaborating effectively with engineering teams.
Highest-signal resume keywords
Python ProgrammingMachine Learning ConceptsSupervised LearningData ProcessingModel Evaluation Metrics
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
PythonMachine LearningData ProcessingModel TrainingModel ValidationSQLGitFeature EngineeringClassification AlgorithmsRegression Algorithms
Soft Skills
Analytical ThinkingProblem-SolvingAttention to DetailCollaborationOpenness to Feedback
Tools & Technologies
NumPyPandasScikit-learnPyTorchTensorFlowJupyter NotebooksAWSGoogle CloudAzureMLflow
Industry Keywords
Machine Learning ApplicationsHealthcare TechnologyData PrivacyResponsible Machine LearningPredictive Analytics
Tech Stack
Tools & technologiesAWSAzureCloudNumpyPandasPythonPyTorchScikit-LearnSQLTensorflow
About the role
Key responsibilities & impact- Assist with developing machine learning models and supporting engineering systems across Bask Health.
- Work with experienced engineers to understand practical machine learning development workflows.
- Prepare, clean, and transform approved datasets for model training and evaluation.
- Write Python scripts for data processing, experimentation, and model development.
- Assist with implementing supervised learning models and baseline algorithms.
- Explore classification, regression, ranking, and other machine learning approaches.
- Help develop feature engineering pipelines for machine learning applications.
- Assist with training, validating, and evaluating machine learning models.
- Compare model performance using appropriate evaluation metrics.
- Help investigate model errors and unexpected predictions.
- Support the development of reproducible machine learning experiments.
- Document model configurations, datasets, assumptions, and evaluation results.
- Assist with building automated data validation and model testing workflows.
- Learn how machine learning models are integrated into backend services and APIs.
- Contribute to small machine learning application features under engineering guidance.
- Assist with model inference testing and performance measurement.
- Help monitor model behavior and investigate reliability issues.
- Work with engineers to understand model deployment and production monitoring processes.
- Assist with developing scripts and utilities that support ML engineering workflows.
- Explore techniques for improving model accuracy, efficiency, and generalization.
- Help identify data quality issues that affect model performance.
- Participate in code reviews and incorporate feedback from experienced engineers.
- Learn and follow Bask Health's software engineering and machine learning development standards.
- Use version control and collaborative development tools to manage assigned work.
- Collaborate with software engineers and product teams to understand machine learning use cases.
- Present experiment results, technical findings, and project progress to team members.
- Learn about responsible machine learning practices and secure data handling.
- Complete assigned machine learning engineering projects with mentorship, documentation, and appropriate testing.
Requirements
What you’ll need- Currently pursuing, recently completed, or independently developing skills equivalent to a degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, Software Engineering, Mathematics, Statistics, or a related technical field.
- Foundational programming skills in Python or a comparable language.
- Basic understanding of machine learning concepts and common algorithms.
- Familiarity with supervised learning, including classification or regression.
- Understanding of basic programming principles and data structures.
- Familiarity with data processing and manipulation techniques.
- Basic knowledge of Python libraries such as NumPy, pandas, or comparable tools.
- Understanding of model training, validation, and testing concepts.
- Familiarity with common machine learning evaluation metrics.
- Basic understanding of probability, statistics, or linear algebra.
- Ability to write and debug Python scripts.
- Familiarity with structured datasets and tabular data.
- Basic understanding of SQL or willingness to learn database querying.
- Familiarity with Git or willingness to learn version control workflows.
- Ability to read technical documentation and follow implementation instructions.
- Interest in building practical machine learning applications.
- Willingness to learn machine learning frameworks and development tools.
- Understanding of basic software testing and debugging practices.
- Ability to explain technical concepts and experiment results clearly.
- Strong analytical thinking and problem-solving skills.
- Attention to detail when working with datasets and model outputs.
- Ability to collaborate with engineers and other technical team members.
- Openness to feedback and mentorship.
- Ability to manage assigned tasks and communicate progress.
- Strong curiosity about production machine learning systems and their real-world applications.
- Nice to Have: Academic, personal, research, or internship projects involving machine learning.
- Nice to Have: Experience using scikit-learn, PyTorch, TensorFlow, or comparable ML frameworks.
- Nice to Have: Familiarity with model training and hyperparameter tuning.
- Nice to Have: Experience developing classification, regression, or recommendation models.
- Nice to Have: Basic understanding of neural networks and deep learning.
- Nice to Have: Familiarity with natural language processing or text classification.
- Nice to Have: Experience working with Jupyter notebooks or comparable development environments.
- Nice to Have: Familiarity with data visualization libraries.
- Nice to Have: Experience using SQL to query and analyze datasets.
- Nice to Have: Exposure to feature engineering and data preprocessing techniques.
- Nice to Have: Familiarity with model evaluation metrics such as precision, recall, F1 score, or ROC-AUC.
- Nice to Have: Experience using GitHub to share programming or machine learning projects.
- Nice to Have: Exposure to cloud platforms such as AWS, Google Cloud, or Azure.
- Nice to Have: Familiarity with MLflow, Weights & Biases, or comparable experiment tracking tools.
- Nice to Have: Basic understanding of model deployment and inference APIs.
- Nice to Have: Experience participating in machine learning competitions, hackathons, or research projects.
- Nice to Have: Familiarity with recommendation systems, ranking models, or predictive analytics.
- Nice to Have: Interest in healthcare technology, telehealth, or e-commerce applications.
- Nice to Have: Basic awareness of data privacy, model bias, and responsible machine learning.
- Nice to Have: Experience using AI-assisted development tools such as Cursor or Claude Code.