Senior Machine Learning Engineer, Search & Personalization

Bask Health

. Design, build, and deploy machine learning systems for search, discovery, ranking, and personalization.

Posted 11/12/2026full-timeRemote • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying machine learning systems, particularly in search and recommendation applications. Proficient in building scalable search infrastructure and optimizing retrieval pipelines using advanced techniques such as embeddings and hybrid search methods.

Highest-signal resume keywords

Machine Learning EngineeringSearch Infrastructure DesignPython ProgrammingInformation Retrieval SystemsRanking Algorithms

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning SystemsSearch AlgorithmsEmbeddingsVector SearchSemantic SearchRanking ModelsData ProcessingFeature EngineeringBackend APIsCloud Infrastructure

Soft Skills

Analytical ThinkingCommunication SkillsTechnical Problem-SolvingCollaborationMentoring

Tools & Technologies

ElasticsearchOpenSearchPineconeWeaviateMilvusPgvectorAWSMonitoring ToolsLogging ToolsVersion Control Systems

Industry Keywords

Healthcare Data PrivacyHIPAA ComplianceRecommendation SystemsSearch Relevance OptimizationUser Behavior Modeling

Tech Stack

Tools & technologies

AWSCloudElasticSearchPython

About the role

Key responsibilities & impact

Design, build, and deploy machine learning systems for search, discovery, ranking, and personalization.
Develop semantic search capabilities across Bask Health's applications.
Build and optimize retrieval pipelines using embeddings, vector search, and traditional search methods.
Design hybrid search systems combining lexical matching, semantic retrieval, and relevance ranking.
Develop ranking models and algorithms that prioritize useful search results.
Build recommendation systems for platform features, resources, or workflow information.
Develop contextual personalization using approved user and application signals.
Identify opportunities to improve search relevance and information discovery with product teams.
Build scalable indexing, retrieval, and search-serving infrastructure.
Design data pipelines supporting search models, ranking systems, and recommendation features.
Generate, store, update, and evaluate embeddings.
Experiment with learning-to-rank methods and machine learning-based relevance improvements.
Implement query understanding, query expansion, and search intent interpretation.
Improve search performance across structured and unstructured data sources.
Support retrieval-augmented generation applications with applied AI engineers.
Develop reusable retrieval services for patient, merchant, and operational applications.
Define and track search quality metrics, including relevance, recall, precision, and ranking performance.
Design offline evaluation datasets and benchmarking procedures.
Conduct controlled experiments and measure user experience improvements.
Optimize search latency, throughput, infrastructure utilization, and operational costs.
Implement monitoring and observability for production search and personalization services.
Investigate search quality issues, indexing failures, and ranking inconsistencies.
Ensure systems respect user permissions and data access boundaries.
Design safeguards for sensitive healthcare-related recommendations.
Integrate search and recommendation features into production applications with backend and frontend engineers.
Contribute to architecture decisions involving search infrastructure, databases, and machine learning services.
Mentor engineers and share expertise in information retrieval, ranking, and production ML engineering.
Own search and personalization initiatives from technical design through deployment, measurement, and ongoing improvement.

Requirements

What you’ll need

5+ years of professional experience in machine learning engineering, software engineering, search engineering, or a closely related technical discipline.
Demonstrated experience building and deploying machine learning systems in production.
Strong programming skills in Python and experience developing production software services.
Experience building search, recommendation, ranking, or information retrieval systems.
Strong understanding of machine learning fundamentals and applied statistical methods.
Familiarity with modern information retrieval techniques and search relevance optimization.
Experience with embeddings, vector representations, or semantic similarity methods.
Understanding of lexical search, semantic search, and hybrid retrieval approaches.
Experience designing or integrating scalable search infrastructure.
Strong understanding of ranking algorithms and relevance evaluation.
Experience working with structured and unstructured datasets.
Familiarity with data processing, feature engineering, and machine learning pipelines.
Experience building backend APIs and integrating machine learning services into applications.
Understanding of database design, indexing, and query performance.
Experience evaluating model or search system performance using quantitative metrics.
Familiarity with offline evaluation, experimentation, and performance benchmarking.
Experience optimizing production systems for latency, scalability, and reliability.
Understanding of cloud infrastructure and deployment practices.
Familiarity with monitoring, logging, and troubleshooting production services.
Strong software engineering practices, including testing, code review, and version control.
Understanding of data privacy, access controls, and secure application design.
Ability to translate product requirements into practical machine learning solutions.
Experience collaborating with product managers and cross-functional engineering teams.
Ability to independently own complex engineering projects from design through deployment.
Strong analytical thinking, communication skills, and technical problem-solving abilities.
Nice to Have: Experience building personalized recommendation systems for SaaS or consumer applications.
Nice to Have: Familiarity with learning-to-rank models and neural ranking approaches.
Nice to Have: Experience with approximate nearest neighbor search and vector indexing techniques.
Nice to Have: Familiarity with Elasticsearch, OpenSearch, Vespa, or comparable search infrastructure.
Nice to Have: Experience using vector databases such as Pinecone, Weaviate, Milvus, or pgvector.
Nice to Have: Experience with embedding models and transformer-based retrieval systems.
Nice to Have: Familiarity with cross-encoders, reranking models, and multi-stage retrieval pipelines.
Nice to Have: Experience developing query understanding or natural language search features.
Nice to Have: Experience building retrieval systems for large language model applications.
Nice to Have: Familiarity with retrieval-augmented generation architectures.
Nice to Have: Experience with recommendation approaches such as collaborative filtering or content-based ranking.
Nice to Have: Experience with online experimentation, A/B testing, and search relevance measurement.
Nice to Have: Familiarity with clickstream analysis and user behavior modeling.
Nice to Have: Experience developing real-time or near-real-time personalization systems.
Nice to Have: Experience with large-scale data processing frameworks or distributed computing.
Nice to Have: Familiarity with AWS and cloud-based machine learning infrastructure.
Nice to Have: Experience building search or recommendation features for e-commerce platforms.
Nice to Have: Experience with healthcare, telehealth, or other privacy-sensitive software.
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.