The posting
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 an Applied ML Engineer, Care Experience to develop intelligent software capabilities that improve how patients receive support and navigate ongoing care-related experiences through Bask Health's platform.
Bask Health provides the technology behind direct-to-consumer telehealth businesses, connecting patient onboarding, clinical workflows, pharmacy coordination, and ongoing patient interactions. These processes often involve communication, administrative follow-ups, operational handoffs, and coordination between multiple systems and teams.
You'll help build machine learning and AI-powered features that make these experiences more consistent, efficient, and easier to manage for patients and authorized care teams.
This is a hands-on applied machine learning engineering role that combines large language models, predictive modeling, backend development, and workflow automation. You'll work with approved patient-related information, application events, APIs, and model outputs to develop intelligent features that support care-related operations.
Your work may include intelligent message classification, administrative request routing, AI-assisted communication, workflow prioritization, and systems that help authorized teams identify incomplete or delayed operational tasks.
You'll collaborate with product managers, applied AI engineers, machine learning specialists, and clinical or pharmacy operations stakeholders to understand workflow requirements and translate them into reliable software capabilities.
You'll also help evaluate model accuracy, monitor production behavior, and improve machine learning systems based on real-world performance. You'll need to understand when traditional software rules, predictive models, or large language models are most appropriate for a particular problem.
Because these applications support healthcare-related workflows, accuracy, privacy, appropriate permissions, and human oversight are essential. You'll build systems that support authorized professionals and administrative processes without independently diagnosing patients, recommending treatments, prescribing medications, or making clinical decisions.
This position emphasizes practical engineering, measurable improvements, and responsible deployment. You'll contribute to technical design, build production features, and continuously improve systems based on user feedback and evaluation results.
We're looking for an engineer who enjoys applying machine learning to real operational challenges, understands the importance of dependable software in sensitive environments, and wants to help make ongoing care experiences easier to manage.
What You'll Do
- Build and maintain machine learning and AI-powered capabilities that support ongoing patient care experiences.
- Develop intelligent systems for approved patient support and care coordination workflows.
- Implement machine learning models that classify and organize patient-related administrative requests.
- Build AI-assisted tools that help authorized teams understand and manage incoming communications.
- Develop routing and prioritization systems for operational tasks and support requests.
- Implement intelligent workflow assistance for follow-ups, status updates, and administrative coordination.
- Build backend services and APIs that connect ML capabilities to existing platform applications.
- Integrate large language models into approved care-related operational workflows.
- Develop structured output pipelines that convert model responses into validated application data.
- Build information retrieval capabilities that surface relevant approved workflow information.
- Implement AI-assisted summaries for authorized care and operations teams.
- Develop systems that help identify incomplete administrative workflows or delayed operational handoffs.
- Evaluate opportunities to apply predictive modeling to approved care experience use cases.
- Design feature engineering and data processing workflows for machine learning applications.
- Build automated tests for model outputs, APIs, and application behavior.
- Evaluate models for accuracy, relevance, consistency, and operational usefulness.
- Investigate incorrect classifications, unreliable responses, and unexpected model behavior.
- Improve model configurations, prompts, and application logic based on evaluation results.
- Implement human review and confirmation steps for sensitive workflows.
- Ensure ML-powered features respect user permissions and approved data access.
- Collaborate with product and operational stakeholders to define measurable success criteria.
- Monitor production model behavior, application errors, and workflow performance.
- Optimize inference latency, reliability, and operational costs.
- Work with backend engineers to integrate ML services into production infrastructure.
- Support controlled deployment and testing of new intelligent care experience features.
- Document model assumptions, known limitations, and workflow safeguards.
- Participate in code reviews, technical planning, and architecture discussions.
- Own assigned applied ML features from development through deployment and ongoing improvement.
Requirements
- 3+ years of professional experience in machine learning engineering, software engineering, applied AI, or a closely related technical field.
- Experience developing and deploying production software or machine learning applications.
- Strong programming skills in Python, TypeScript, or comparable languages.
- Familiarity with machine learning models and practical model evaluation techniques.
- Experience building AI-powered applications or integrating machine learning capabilities into software products.
- Understanding of large language models and modern generative AI development patterns.
- Experience developing backend services and application APIs.
- Familiarity with model APIs, structured outputs, and application integrations.
- Experience working with relational databases and structured application data.
- Understanding of classification, prediction, or other applied machine learning methods.
- Familiarity with data processing and feature preparation workflows.
- Experience writing automated tests for production software.
- Ability to evaluate model outputs and identify accuracy or reliability problems.
- Understanding of authentication, authorization, and secure application design.
- Familiarity with asynchronous processing and workflow automation.
- Experience with logging, monitoring, and production troubleshooting.
- Understanding of model limitations and common AI failure modes.
- Familiarity with cloud-based application deployment.
- Ability to translate operational requirements into technical solutions.
- Understanding of responsible data handling and privacy-conscious development.
- Experience using Git and collaborative engineering workflows.
- Strong analytical and problem-solving skills.
- Ability to communicate effectively with technical and nontechnical stakeholders.
- Strong attention to detail and software quality.
- Ability to independently own assigned engineering projects.
Nice to Have
- Experience developing machine learning applications for healthcare, telehealth, or patient support.
- Familiarity with care coordination or patient communication workflows.
- Experience with message classification, intent detection, or automated request routing.
- Familiarity with natural language processing and text classification models.
- Experience using OpenAI, Anthropic, or comparable model APIs.
- Familiarity with retrieval-augmented generation architectures.
- Experience with embeddings, semantic search, and vector databases.
- Experience building AI-assisted summarization or communication tools.
- Familiarity with predictive models for operational prioritization or workflow completion.
- Experience with scikit-learn, PyTorch, TensorFlow, or comparable ML frameworks.
- Experience building human-in-the-loop machine learning applications.
- Familiarity with AI evaluation frameworks and model observability.
- Experience with TypeScript and Node.js backend development.
- Experience with React or Next.js applications.
- Familiarity with tRPC, Prisma, Drizzle, or comparable development tools.
- Experience working with AWS or serverless infrastructure.
- Understanding of HIPAA and healthcare data privacy considerations.
- Familiarity with clinical and pharmacy operational workflows.
- Experience implementing audit trails and permission-controlled application features.
- Experience using AI-assisted development tools such as Cursor or Claude Code.
