Machine Learning Engineer, Personalization

Bask Health

. Design, develop, and deploy machine learning models supporting personalized experiences across Bask Health.

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

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and deploying machine learning models, particularly in recommendation systems and personalization strategies. Proficient in Python, machine learning frameworks, and data processing pipelines, with a strong focus on model evaluation and performance optimization.

Highest-signal resume keywords

Machine Learning EngineeringRecommendation SystemsPython ProgrammingModel Evaluation TechniquesData Processing Pipelines

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning ModelsRecommendation AlgorithmsFeature EngineeringSQLModel TrainingA/B TestingCollaborative FilteringContent-Based RecommendationModel MonitoringStatistical Analysis

Soft Skills

Analytical SkillsProblem-Solving SkillsCollaborationProject OwnershipCommunication

Tools & Technologies

PyTorchTensorFlowScikit-learnGitAWS SageMakerCloud-Based DeploymentAPIsProduction CodeMachine Learning FrameworksUser Behavior Modeling

Industry Keywords

PersonalizationHealthcare Data PrivacyHIPAAE-CommerceSaaS PlatformsCold-Start ProblemsMulti-Tenant ArchitectureContextual BanditsReinforcement LearningNDCG

Tech Stack

Tools & technologies

AWSCloudJavaScriptNext.jsNode.jsPythonPyTorchReactScikit-LearnSQLTensorflowTypeScript

About the role

Key responsibilities & impact

Design, develop, and deploy machine learning models supporting personalized experiences across Bask Health.
Build recommendation systems and ranking algorithms for approved platform content and user experiences.
Implement personalization models using behavioral, contextual, and preference-related signals.
Build feature engineering pipelines and systems representing user preferences and interaction patterns.
Implement personalized content ordering and discovery within approved workflows.
Build backend services and APIs delivering personalization results to applications.
Collaborate with frontend engineers on user-facing personalization integrations.
Develop baseline models and compare them with advanced personalization approaches.
Experiment with collaborative filtering, content-based recommendation, and learning-to-rank methods.
Implement model training, validation, evaluation, offline metrics, online experimentation, and A/B testing.
Analyze engagement and interaction data to improve models.
Monitor production model performance, latency, and reliability; investigate quality changes and unexpected behavior.
Handle sparse data and new-user cold-start scenarios, including appropriate fallbacks.
Optimize inference speed and resource usage; support scalable model serving.
Work with product managers to define personalization goals and success metrics.
Evaluate performance across relevant user groups and application contexts.
Implement privacy-conscious data processing and feature access controls while respecting tenant boundaries and approved permissions.
Document model assumptions, evaluation results, and limitations.
Participate in code reviews, technical planning, and architecture discussions.
Own personalization features from model development through deployment, monitoring, and continuous improvement.

Requirements

What you’ll need

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 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.
Nice to have: Familiarity with collaborative filtering and content-based recommendation algorithms.
Nice to have: Experience with learning-to-rank techniques and ranking optimization.
Nice to have: Understanding of embeddings and vector similarity methods.
Nice to have: Experience building personalized search or content discovery features.
Nice to have: Familiarity with user behavior modeling and preference prediction.
Nice to have: Experience developing real-time personalization systems.
Nice to have: Understanding of contextual bandits or reinforcement learning approaches to recommendations.
Nice to have: Experience with A/B testing and online experimentation platforms.
Nice to have: Familiarity with recommendation metrics such as NDCG, MAP, precision@k, and recall@k.
Nice to have: Experience working with sparse interaction data and cold-start problems.
Nice to have: Familiarity with feature stores and machine learning data pipelines.
Nice to have: Experience with AWS SageMaker or comparable ML platform services.
Nice to have: Experience building low-latency model inference services.
Nice to have: Familiarity with TypeScript and Node.js backend development.
Nice to have: Experience with React or Next.js application integrations.
Nice to have: Familiarity with multi-tenant SaaS architecture.
Nice to have: Experience applying personalization in healthcare-related software while respecting clinical boundaries.
Nice to have: Understanding of HIPAA and healthcare data privacy considerations.
Nice to have: Experience using AI-assisted development tools such as Cursor or Claude Code.