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 Engineer, Personalization to build and improve intelligent systems that help Bask Health deliver more relevant and personalized experiences across our platform. Bask Health provides the technology behind direct-to-consumer telehealth businesses, bringing together merchant storefronts, patient experiences, clinical workflows, pharmacy operations, and commerce capabilities. As merchants grow, we want to help them create digital experiences that are responsive to user needs, preferences, and interactions. You'll develop machine learning models and recommendation systems that support personalized content, relevant platform experiences, and intelligent discovery within approved product workflows. This is a hands-on machine learning engineering role that combines model development, data processing, backend integration, experimentation, and production monitoring. You'll work with application data, user interaction signals, machine learning algorithms, and platform services to build personalization capabilities that operate reliably at scale. Your work may include developing ranking models, recommendation algorithms, user preference representations, behavioral feature pipelines, and systems that adapt application experiences based on relevant, authorized signals. You'll collaborate with product managers, backend engineers, frontend engineers, data specialists, and other machine learning engineers to identify personalization opportunities and translate them into production-ready features. You'll also help design experiments to measure whether personalization improves user experience, engagement, and relevant product outcomes. You'll evaluate model quality, investigate performance issues, and continuously improve algorithms based on measured results. Because Bask operates in healthcare-related environments, personalization must respect privacy, data access permissions, and clinical boundaries. You'll ensure that models do not independently determine medical eligibility, recommend treatments, change prescriptions, or override approved clinical workflows. This position emphasizes practical machine learning, strong software engineering fundamentals, and measurable product improvements. You'll contribute to model design, implement production systems, and own features through deployment and ongoing optimization. We're looking for an engineer who enjoys working with recommendation systems, understands the challenges of real-world personalization, and wants to build intelligent product experiences that are useful, reliable, and responsible. What You'll Do
- Design, develop, and deploy machine learning models that support personalized experiences across Bask Health.
- Build recommendation systems for approved platform content and user experiences.
- Develop ranking algorithms that improve the relevance of information presented to users.
- Implement personalization models using behavioral, contextual, and preference-related signals.
- Build feature engineering pipelines that transform application data into model-ready inputs.
- Develop systems for representing user preferences and interaction patterns.
- Implement personalized content ordering and discovery capabilities within approved workflows.
- Build backend services and APIs that deliver personalization results to applications.
- Collaborate with frontend engineers to integrate personalization into user-facing experiences.
- Develop baseline models and compare them against more advanced personalization approaches.
- Experiment with collaborative filtering, content-based recommendation, and learning-to-rank methods.
- Implement model training, validation, and evaluation workflows.
- Develop offline evaluation methods for recommendation and ranking quality.
- Support online experimentation and A/B testing for personalization features.
- Analyze engagement and interaction data to identify opportunities for model improvements.
- Monitor production model performance, latency, and reliability.
- Investigate changes in recommendation quality and unexpected model behavior.
- Develop strategies for handling sparse data and new-user cold-start scenarios.
- Implement appropriate fallbacks when personalization signals are unavailable.
- Optimize model inference speed and resource usage.
- Collaborate with backend engineers to support scalable model serving.
- Work with product managers to define personalization goals and success metrics.
- Evaluate model performance across relevant user groups and application contexts.
- Implement privacy-conscious data processing and feature access controls.
- Ensure personalization features respect tenant boundaries and approved platform permissions.
- Document model assumptions, evaluation results, and known limitations.
- Participate in code reviews, technical planning, and architecture discussions.
- Own assigned personalization features from model development through deployment, monitoring, and continuous improvement.
Requirements
- 3+ years of professional experience in machine learning engineering, applied machine learning, software engineering, or a closely related technical field.
- Experience developing and deploying machine learning models in production.
- Strong programming skills in Python or comparable machine learning development languages.
- Understanding of recommendation systems, ranking models, or predictive modeling.
- Experience working with machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn.
- Familiarity with supervised learning and common model evaluation techniques.
- Experience developing data processing and feature engineering pipelines.
- Strong understanding of SQL and structured application data.
- Familiarity with statistical analysis and experimental design.
- Experience integrating machine learning models into production applications.
- Understanding of backend services and API development.
- Familiarity with cloud-based machine learning deployment.
- Experience evaluating model quality using appropriate offline metrics.
- Understanding of model overfitting, data leakage, and generalization.
- Familiarity with model training, validation, and testing workflows.
- Experience troubleshooting machine learning performance issues.
- Understanding of data quality and feature reliability.
- Familiarity with model monitoring and production observability.
- Experience writing maintainable, tested production code.
- Understanding of authentication, authorization, and secure data handling.
- Experience using Git and collaborative engineering workflows.
- Ability to translate product requirements into machine learning solutions.
- Strong analytical and problem-solving skills.
- Ability to collaborate effectively with product managers, data specialists, and software engineers.
- Ability to independently own machine learning projects from development through deployment.
Nice to Have
- Experience building recommendation systems for e-commerce or SaaS platforms.
- Familiarity with collaborative filtering and content-based recommendation algorithms.
- Experience with learning-to-rank techniques and ranking optimization.
- Understanding of embeddings and vector similarity methods.
- Experience building personalized search or content discovery features.
- Familiarity with user behavior modeling and preference prediction.
- Experience developing real-time personalization systems.
- Understanding of contextual bandits or reinforcement learning approaches to recommendations.
- Experience with A/B testing and online experimentation platforms.
- Familiarity with recommendation metrics such as NDCG, MAP, precision@k, and recall@k.
- Experience working with sparse interaction data and cold-start problems.
- Familiarity with feature stores and machine learning data pipelines.
- Experience with AWS SageMaker or comparable ML platform services.
- Experience building low-latency model inference services.
- Familiarity with TypeScript and Node.js backend development.
- Experience with React or Next.js application integrations.
- Familiarity with multi-tenant SaaS architecture.
- Experience applying personalization in healthcare-related software while respecting clinical boundaries.
- Understanding of HIPAA and healthcare data privacy considerations.
- Experience using AI-assisted development tools such as Cursor or Claude Code.
