Applied ML Engineer, Care Experience

Bask Health

. Build and maintain machine learning and AI-powered capabilities that support ongoing patient care experiences.

Posted 11/12/2026full-timeRemote • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in machine learning engineering and AI application development, with a strong focus on building and integrating intelligent systems that enhance patient care workflows. Proficient in Python and TypeScript, with a solid understanding of model evaluation, backend services, and secure application design.

Highest-signal resume keywords

Machine Learning EngineeringPython ProgrammingAI Application DevelopmentBackend Services DevelopmentModel Evaluation Techniques

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningAI-Powered ApplicationsProduction Software DevelopmentModel APIsData Processing WorkflowsAutomated TestingRelational DatabasesClassification MethodsFeature EngineeringAsynchronous Processing

Soft Skills

Analytical SkillsProblem-Solving SkillsEffective CommunicationAttention to DetailProject Ownership

Tools & Technologies

GitCloud-Based DeploymentMonitoring ToolsLogging ToolsWorkflow Automation Tools

Industry Keywords

Patient CareOperational WorkflowsGenerative AIData PrivacySecure Application Design

Tech Stack

Tools & technologies

CloudPythonTypeScript

About the role

Key responsibilities & impact

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

What you’ll need

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.