Machine Learning Engineer, Personalization

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