Machine Learning Engineer, Search & Discovery

Bask Health

. Build and maintain intelligent search and discovery capabilities across Bask Health's applications .

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

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building and maintaining intelligent search systems, including semantic search and hybrid retrieval methods. Proficient in developing APIs, optimizing search infrastructure, and ensuring data quality and relevance in search results.

Highest-signal resume keywords

Python ProgrammingSemantic Search ImplementationSearch Indexing and Query ProcessingAPI DevelopmentMachine Learning for Information Retrieval

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Applicant Tracking System Keywords

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Hard Skills

TypeScript ProgrammingSearch System FundamentalsBackend Services DevelopmentData Pipeline ManagementAutomated TestingDatabase QueryingSearch Relevance EvaluationVector-Based Similarity SearchTroubleshooting Application PerformanceCollaborative Software Development

Soft Skills

Analytical Problem-SolvingEffective CollaborationProject Ownership

Tools & Technologies

ElasticsearchOpenSearchPineconeWeaviateQdrantPgvectorAWSNode.js

Industry Keywords

Information RetrievalSearch Quality EvaluationHealthcare Data PrivacyMulti-Tenant ApplicationsAI-Assisted Development Tools

Tech Stack

Tools & technologies

AWSCloudElasticSearchJavaScriptNode.jsPythonTypeScript

About the role

Key responsibilities & impact

Build and maintain intelligent search and discovery capabilities across Bask Health's applications
Develop search services that retrieve relevant information from approved platform data sources
Implement semantic search using embeddings and vector-based retrieval
Build hybrid search systems combining keyword matching with semantic retrieval
Develop ranking and reranking approaches to improve search result relevance
Implement query processing, normalization, and understanding capabilities
Build search indexes and data pipelines that keep searchable information accurate and current
Develop APIs exposing search and retrieval functionality to other applications
Integrate search capabilities into merchant-facing and internal platform tools
Support information retrieval for AI assistants and retrieval-augmented generation systems
Implement filtering and metadata-based retrieval for structured application information
Ensure search functionality respects user permissions and tenant boundaries
Build automated tests for search behavior, retrieval quality, and ranking performance
Create evaluation datasets representing realistic search queries and expected results
Measure search relevance using retrieval and ranking metrics
Analyze search failures, missing results, and low-quality rankings
Improve search quality through experimentation and iterative model or algorithm changes
Evaluate embedding models and retrieval configurations for different use cases
Implement monitoring for search latency, availability, and retrieval performance
Optimize search infrastructure for efficient indexing and query execution
Work with backend engineers to integrate search services into existing application architecture
Collaborate with product teams to identify high-value search and discovery use cases
Develop tools for debugging search results and understanding ranking behavior
Support search-related experimentation and controlled feature rollouts
Improve the reliability and maintainability of search pipelines and services
Participate in code reviews, technical planning, and architecture discussions
Document search configurations, evaluation methods, and implementation decisions
Own assigned search and discovery projects from development through deployment and continuous improvement

Requirements

What you’ll need

3+ years of professional experience in machine learning engineering, software engineering, search engineering, or a closely related technical field
Experience building and maintaining production software applications or data-driven systems
Strong programming skills in Python, TypeScript, or comparable languages
Familiarity with information retrieval and search system fundamentals
Experience developing backend services and application APIs
Understanding of search indexing, query processing, and ranking concepts
Familiarity with machine learning methods used for search or information retrieval
Experience working with structured and unstructured datasets
Understanding of database queries and application data models
Familiarity with embeddings and vector-based similarity search
Experience integrating search functionality into software applications
Understanding of search relevance and retrieval quality evaluation
Familiarity with automated testing and production software development practices
Experience troubleshooting application performance and data quality issues
Understanding of authentication, authorization, and secure data access
Familiarity with cloud-based application deployment
Experience working with Git and collaborative software development workflows
Ability to analyze search results and identify relevance problems
Understanding of system performance, latency, and scalability considerations
Experience writing maintainable, well-tested production code
Familiarity with logging, monitoring, and production troubleshooting
Ability to translate product requirements into technical search functionality
Strong analytical and problem-solving skills
Ability to collaborate effectively with product managers and engineers
Ability to independently own assigned technical projects
Nice to Have: Experience building semantic search or hybrid search applications
Nice to Have: Familiarity with Elasticsearch, OpenSearch, or comparable search technologies
Nice to Have: Experience with vector databases such as Pinecone, Weaviate, Qdrant, or pgvector
Nice to Have: Experience implementing retrieval-augmented generation systems
Nice to Have: Familiarity with embedding models and vector similarity techniques
Nice to Have: Experience with learning-to-rank or neural reranking approaches
Nice to Have: Understanding of BM25 and traditional information retrieval algorithms
Nice to Have: Familiarity with search evaluation metrics such as precision, recall, MRR, and NDCG
Nice to Have: Experience building search relevance evaluation datasets
Nice to Have: Experience with query rewriting, expansion, or intent classification
Nice to Have: Familiarity with large language models and AI-powered search applications
Nice to Have: Experience with OpenAI, Anthropic, or comparable model APIs
Nice to Have: Experience building search features for SaaS or e-commerce platforms
Nice to Have: Familiarity with product catalogs, merchant data, or operational knowledge systems
Nice to Have: Experience with TypeScript and Node.js backend development
Nice to Have: Experience working with AWS or serverless infrastructure
Nice to Have: Familiarity with data pipelines and event-driven indexing
Nice to Have: Experience implementing permission-aware retrieval in multi-tenant applications
Nice to Have: Understanding of healthcare data privacy considerations
Nice to Have: Experience using AI-assisted development tools such as Cursor or Claude Code