Machine Learning Engineering Intern

Job not on LinkedIn

🔥 5 minutes ago

🇺🇸 United States – Remote

👨‍🎓 Internship

⚪️ Entry-level

🤖 Machine Learning Engineer

🚫👨‍🎓 No degree required

👻 Ghost score 25%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.Why this score:

• Just posted.
• Jobs at this company usually fill in 60 days.
• 102 open roles for a team of 16.
• No salary listed.

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Bask Health

WebsiteLinkedInAll Job Openings

11 - 50 employees

🏥 Healthcare

💼 Consulting

📦 Logistics

💰 Pre Seed Round on 2022-05

Healthcare • Consulting • Logistics

Bask Health is a robust platform providing a comprehensive suite of customizable telehealth solutions. It offers tools such as a questionnaire builder, patient portals, and pharmacy fulfillment services, all secured with strong encryption and HIPAA-compliance. Bask Health seamlessly integrates telehealth and e-commerce, enabling streamlined patient care from consultation to delivery. The platform supports entrepreneurs, doctors, and developers by offering scalable no-code solutions that enhance user experience and business growth. With its nationwide pharmacy network, Bask Health empowers businesses to efficiently manage prescriptions, patient data, and order fulfillment across the entire digital health landscape.

📋 Description

• 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. • 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.

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