About Bask Health
Bask Health is a software platform that enables entrepreneurs, doctors, and healthcare businesses to launch, manage, and grow direct-to-consumer telehealth businesses.
Our platform brings together patient experiences, clinical workflows, pharmacy operations, and commerce tools in one place.
We're an entrepreneurial, product-focused engineering team that values ownership, technical excellence, and building software that solves real problems. Engineers work closely with product teams and company leadership, take features from idea to production, and have a direct role in shaping the platform.
Engineering at Bask is AI-first and builder-led. We use tools like Cursor and Claude Code throughout development while maintaining high standards for reliability, testing, and software quality.
About the Role
We're looking for a Machine Learning Engineering Intern to join Bask Health's engineering team and gain practical experience developing machine learning models, working with real application data, and supporting production ML systems.
Bask Health provides the technology behind direct-to-consumer telehealth businesses, connecting merchant storefronts, patient experiences, clinical workflows, pharmacy operations, and commerce capabilities. As we expand our machine learning capabilities, we're looking for motivated individuals who want to learn how intelligent systems are developed, evaluated, and integrated into real software products.
You'll work alongside experienced machine learning engineers, applied AI engineers, software engineers, and product teams to contribute to machine learning projects across the platform.
This internship is designed to provide hands-on engineering experience. Rather than focusing exclusively on theoretical research, you'll learn how machine learning models are developed, tested, improved, and prepared for use in production applications.
Your work may include preparing datasets, exploring model architectures, implementing training scripts, evaluating model performance, and helping integrate machine learning capabilities into existing software systems.
You'll gain exposure to practical machine learning workflows, including feature engineering, experiment tracking, model validation, performance monitoring, and reproducible development practices.
You'll also learn how engineering teams evaluate whether a machine learning solution is appropriate for a product problem, compare model approaches, and balance accuracy with reliability, latency, and operational cost.
You'll receive guidance from experienced engineers while contributing to appropriately scoped projects. As you develop your skills, you'll have opportunities to take greater ownership of technical tasks and communicate your findings to the team.
Because Bask operates in healthcare-related environments, you'll learn how privacy, secure data handling, appropriate permissions, and human oversight affect machine learning development. You'll work with approved datasets and established safeguards, and your work will not involve independently making clinical decisions or determining patient treatment.
We welcome candidates who have developed machine learning skills through university coursework, independent projects, research, internships, hackathons, or self-directed learning. Previous professional machine learning engineering experience is not required.
We're looking for someone who enjoys programming, is curious about how machine learning systems work, and wants to develop the practical engineering skills needed to build reliable intelligent software.
What You'll Do
- 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
- 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.
- Experience using scikit-learn, PyTorch, TensorFlow, or comparable ML frameworks.
- Familiarity with model training and hyperparameter tuning.
- Experience developing classification, regression, or recommendation models.
- Basic understanding of neural networks and deep learning.
- Familiarity with natural language processing or text classification.
- Experience working with Jupyter notebooks or comparable development environments.
- Familiarity with data visualization libraries.
- Experience using SQL to query and analyze datasets.
- Exposure to feature engineering and data preprocessing techniques.
- Familiarity with model evaluation metrics such as precision, recall, F1 score, or ROC-AUC.
- Experience using GitHub to share programming or machine learning projects.
- Exposure to cloud platforms such as AWS, Google Cloud, or Azure.
- Familiarity with MLflow, Weights & Biases, or comparable experiment tracking tools.
- Basic understanding of model deployment and inference APIs.
- Experience participating in machine learning competitions, hackathons, or research projects.
- Familiarity with recommendation systems, ranking models, or predictive analytics.
- Interest in healthcare technology, telehealth, or e-commerce applications.
- Basic awareness of data privacy, model bias, and responsible machine learning.
- Experience using AI-assisted development tools such as Cursor or Claude Code.