Machine Learning Engineer, Search & Discovery

Job not on LinkedIn

🔥 11 minutes ago

🇺🇸 United States – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 25%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.Why this score:

• Just posted.
• Jobs at this company usually fill in 60 days.
• 102 open roles for a team of 16.
• No salary listed.

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Bask Health

WebsiteLinkedInAll Job Openings

11 - 50 employees

🏥 Healthcare

💼 Consulting

📦 Logistics

💰 Pre Seed Round on 2022-05

Healthcare • Consulting • Logistics

Bask Health is a robust platform providing a comprehensive suite of customizable telehealth solutions. It offers tools such as a questionnaire builder, patient portals, and pharmacy fulfillment services, all secured with strong encryption and HIPAA-compliance. Bask Health seamlessly integrates telehealth and e-commerce, enabling streamlined patient care from consultation to delivery. The platform supports entrepreneurs, doctors, and developers by offering scalable no-code solutions that enhance user experience and business growth. With its nationwide pharmacy network, Bask Health empowers businesses to efficiently manage prescriptions, patient data, and order fulfillment across the entire digital health landscape.

📋 Description

• 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

• 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

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