{"id":4190825,"url":"https://alion.io/job/bask-health-machine-learning-engineering-intern","title":"Machine Learning Engineering Intern","company":{"id":66537,"name":"Bask Health","domain":"bask.health","url":"https://alion.io/company/bask-health","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"A","score":91,"open_postings":21,"ghost_share":0,"stale_share":0.143,"repost_share":0,"time_to_fill_p50_days":171,"computed_at":"2026-11-12T10:38:37Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"intern","employment_type":"internship","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Claude Code","optional":false},{"name":"Cursor","optional":false},{"name":"Git","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"GCP","optional":true},{"name":"GitHub","optional":true},{"name":"MLFlow","optional":true},{"name":"PyTorch","optional":true},{"name":"Recommender Systems","optional":true},{"name":"Scikit-learn","optional":true},{"name":"TensorFlow","optional":true},{"name":"Weights & Biases","optional":true}],"status":"live","first_seen_at":"2026-11-12T19:17:00Z","employer_posted_date":"2026-11-12","last_verified_at":"2026-11-12T19:17:00Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"About Bask Health\nBask Health is a software platform that enables entrepreneurs, doctors, and healthcare businesses to launch, manage, and grow direct-to-consumer telehealth businesses.\nOur platform brings together patient experiences, clinical workflows, pharmacy operations, and commerce tools in one place.\nWe'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.\nEngineering 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.\nAbout the Role\nWe'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.\nBask 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.\nYou'll work alongside experienced machine learning engineers, applied AI engineers, software engineers, and product teams to contribute to machine learning projects across the platform.\nThis 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.\nYour work may include preparing datasets, exploring model architectures, implementing training scripts, evaluating model performance, and helping integrate machine learning capabilities into existing software systems.\nYou'll gain exposure to practical machine learning workflows, including feature engineering, experiment tracking, model validation, performance monitoring, and reproducible development practices.\nYou'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.\nYou'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.\nBecause 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.\nWe 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.\nWe'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.\nWhat You'll Do\nAssist with developing machine learning models and supporting engineering systems across Bask Health.\nWork with experienced engineers to understand practical machine learning development workflows.\nPrepare, clean, and transform approved datasets for model training and evaluation.\nWrite Python scripts for data processing, experimentation, and model development.\nAssist with implementing supervised learning models and baseline algorithms.\nExplore classification, regression, ranking, and other machine learning approaches.\nHelp develop feature engineering pipelines for machine learning applications.\nAssist with training, validating, and evaluating machine learning models.\nCompare model performance using appropriate evaluation metrics.\nHelp investigate model errors and unexpected predictions.\nSupport the development of reproducible machine learning experiments.\nDocument model configurations, datasets, assumptions, and evaluation results.\nAssist with building automated data validation and model testing workflows.\nLearn how machine learning models are integrated into backend services and APIs.\nContribute to small machine learning application features under engineering guidance.\nAssist with model inference testing and performance measurement.\nHelp monitor model behavior and investigate reliability issues.\nWork with engineers to understand model deployment and production monitoring processes.\nAssist with developing scripts and utilities that support ML engineering workflows.\nExplore techniques for improving model accuracy, efficiency, and generalization.\nHelp identify data quality issues that affect model performance.\nParticipate in code reviews and incorporate feedback from experienced engineers.\nLearn and follow Bask Health's software engineering and machine learning development standards.\nUse version control and collaborative development tools to manage assigned work.\nCollaborate with software engineers and product teams to understand machine learning use cases.\nPresent experiment results, technical findings, and project progress to team members.\nLearn about responsible machine learning practices and secure data handling.\nComplete assigned machine learning engineering projects with mentorship, documentation, and appropriate testing.\nRequirements\nCurrently 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.\nFoundational programming skills in Python or a comparable language.\nBasic understanding of machine learning concepts and common algorithms.\nFamiliarity with supervised learning, including classification or regression.\nUnderstanding of basic programming principles and data structures.\nFamiliarity with data processing and manipulation techniques.\nBasic knowledge of Python libraries such as NumPy, pandas, or comparable tools.\nUnderstanding of model training, validation, and testing concepts.\nFamiliarity with common machine learning evaluation metrics.\nBasic understanding of probability, statistics, or linear algebra.\nAbility to write and debug Python scripts.\nFamiliarity with structured datasets and tabular data.\nBasic understanding of SQL or willingness to learn database querying.\nFamiliarity with Git or willingness to learn version control workflows.\nAbility to read technical documentation and follow implementation instructions.\nInterest in building practical machine learning applications.\nWillingness to learn machine learning frameworks and development tools.\nUnderstanding of basic software testing and debugging practices.\nAbility to explain technical concepts and experiment results clearly.\nStrong analytical thinking and problem-solving skills.\nAttention to detail when working with datasets and model outputs.\nAbility to collaborate with engineers and other technical team members.\nOpenness to feedback and mentorship.\nAbility to manage assigned tasks and communicate progress.\nStrong curiosity about production machine learning systems and their real-world applications.\nNice to Have\nAcademic, personal, research, or internship projects involving machine learning.\nExperience using scikit-learn, PyTorch, TensorFlow, or comparable ML frameworks.\nFamiliarity with model training and hyperparameter tuning.\nExperience developing classification, regression, or recommendation models.\nBasic understanding of neural networks and deep learning.\nFamiliarity with natural language processing or text classification.\nExperience working with Jupyter notebooks or comparable development environments.\nFamiliarity with data visualization libraries.\nExperience using SQL to query and analyze datasets.\nExposure to feature engineering and data preprocessing techniques.\nFamiliarity with model evaluation metrics such as precision, recall, F1 score, or ROC-AUC.\nExperience using GitHub to share programming or machine learning projects.\nExposure to cloud platforms such as AWS, Google Cloud, or Azure.\nFamiliarity with MLflow, Weights & Biases, or comparable experiment tracking tools.\nBasic understanding of model deployment and inference APIs.\nExperience participating in machine learning competitions, hackathons, or research projects.\nFamiliarity with recommendation systems, ranking models, or predictive analytics.\nInterest in healthcare technology, telehealth, or e-commerce applications.\nBasic awareness of data privacy, model bias, and responsible machine learning.\nExperience using AI-assisted development tools such as Cursor or Claude 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