{"id":4190824,"url":"https://alion.io/job/bask-health-machine-learning-engineer-search-discovery","title":"Machine Learning Engineer, Search & Discovery","company":{"id":66537,"name":"Bask Health","domain":"bask.health","url":"https://alion.io/company/bask-health","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"A","score":88,"open_postings":21,"ghost_share":0,"stale_share":0.286,"repost_share":0,"time_to_fill_p50_days":171,"computed_at":"2026-11-15T07:52:55Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Claude Code","optional":false},{"name":"Cursor","optional":false},{"name":"Embeddings","optional":false},{"name":"Git","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Semantic Search","optional":false},{"name":"TypeScript","optional":false},{"name":"Anthropic","optional":true},{"name":"AWS","optional":true},{"name":"ElasticSearch","optional":true},{"name":"JavaScript","optional":true},{"name":"Node JS","optional":true},{"name":"OpenAI","optional":true},{"name":"OpenSearch","optional":true},{"name":"pgvector","optional":true},{"name":"Pinecone","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Qdrant","optional":true},{"name":"Weaviate","optional":true}],"status":"live","first_seen_at":"2026-11-12T19:17:00Z","employer_posted_date":"2026-11-12","last_verified_at":"2026-11-15T03:21:16Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"About Bask Health\nBask Health is a software platform that enables entrepreneurs, doctors, and healthcare businesses to launch, manage, and grow direct-to-consumer telehealth businesses.\nOur platform brings together patient experiences, clinical workflows, pharmacy operations, and commerce tools in one place.\nWe're an entrepreneurial, product-focused engineering team that values ownership, technical excellence, and building software that solves real problems. Engineers work closely with product teams and company leadership, take features from idea to production, and have a direct role in shaping the platform.\nEngineering at Bask is AI-first and builder-led. We use tools like Cursor and Claude Code throughout development while maintaining high standards for reliability, testing, and software quality.\nAbout the Role\nWe're looking for a Machine Learning Engineer, Search & Discovery to help build intelligent systems that make information easier to find, understand, and use across Bask Health's platform.\nBask Health brings together patient experiences, merchant operations, clinical workflows, pharmacy coordination, and commerce tools. As the platform grows, users and internal teams need efficient ways to locate relevant information, navigate complex workflows, and discover the resources they need.\nYou'll help develop machine learning-powered search and discovery capabilities that improve how information is retrieved, ranked, and presented across our applications.\nThis is a hands-on engineering role that combines information retrieval, machine learning, backend development, and modern AI technologies. You'll work with search indexes, embeddings, vector databases, ranking algorithms, APIs, and structured application data to build reliable search experiences.\nYour work may include semantic search, hybrid keyword and vector retrieval, intelligent filtering, query understanding, search relevance improvements, and retrieval systems that support AI-powered assistants.\nYou'll collaborate with product managers, backend engineers, applied AI engineers, and other machine learning specialists to understand user needs and translate them into measurable search improvements.\nYou'll also help establish evaluation methods for search quality, including relevance testing, ranking metrics, query analysis, and performance monitoring. You'll use real-world usage patterns and evaluation results to identify opportunities to improve retrieval accuracy and user experience.\nBecause Bask operates in healthcare-related environments, search systems must respect appropriate access controls, data privacy requirements, and tenant boundaries. You'll help ensure users only retrieve information they are authorized to access.\nThis position emphasizes practical implementation and continuous improvement. You'll work with senior engineers on more complex architectural decisions while independently delivering search features, retrieval services, and ranking improvements.\nWe're looking for an engineer who enjoys solving information retrieval problems, understands the tradeoffs between search relevance and system performance, and wants to build search experiences that are genuinely useful.\nWhat You'll Do\nBuild and maintain intelligent search and discovery capabilities across Bask Health's applications.\nDevelop search services that retrieve relevant information from approved platform data sources.\nImplement semantic search using embeddings and vector-based retrieval.\nBuild hybrid search systems that combine keyword matching with semantic retrieval.\nDevelop ranking and reranking approaches that improve search result relevance.\nImplement query processing, normalization, and understanding capabilities.\nBuild search indexes and data pipelines that keep searchable information accurate and up to date.\nDevelop APIs that expose search and retrieval functionality to other applications.\nIntegrate search capabilities into merchant-facing and internal platform tools.\nSupport information retrieval for AI assistants and retrieval-augmented generation systems.\nImplement filtering and metadata-based retrieval for structured application information.\nDevelop search functionality that respects user permissions and tenant boundaries.\nBuild automated tests for search behavior, retrieval quality, and ranking performance.\nCreate evaluation datasets that represent realistic search queries and expected results.\nMeasure search relevance using appropriate retrieval and ranking metrics.\nAnalyze search failures, missing results, and low-quality rankings.\nImprove search quality through experimentation and iterative model or algorithm changes.\nEvaluate embedding models and retrieval configurations for different use cases.\nImplement monitoring for search latency, availability, and retrieval performance.\nOptimize search infrastructure for efficient indexing and query execution.\nWork with backend engineers to integrate search services into existing application architecture.\nCollaborate with product teams to identify high-value search and discovery use cases.\nDevelop tools for debugging search results and understanding ranking behavior.\nSupport search-related experimentation and controlled feature rollouts.\nImprove the reliability and maintainability of search pipelines and services.\nParticipate in code reviews, technical planning, and architecture discussions.\nDocument search configurations, evaluation methods, and implementation decisions.\nOwn assigned search and discovery projects from development through deployment and continuous improvement.\nRequirements\n3+ years of professional experience in machine learning engineering, software engineering, search engineering, or a closely related technical field.\nExperience building and maintaining production software applications or data-driven systems.\nStrong programming skills in Python, TypeScript, or comparable languages.\nFamiliarity with information retrieval and search system fundamentals.\nExperience developing backend services and application APIs.\nUnderstanding of search indexing, query processing, and ranking concepts.\nFamiliarity with machine learning methods used for search or information retrieval.\nExperience working with structured and unstructured datasets.\nUnderstanding of database queries and application data models.\nFamiliarity with embeddings and vector-based similarity search.\nExperience integrating search functionality into software applications.\nUnderstanding of search relevance and retrieval quality evaluation.\nFamiliarity with automated testing and production software development practices.\nExperience troubleshooting application performance and data quality issues.\nUnderstanding of authentication, authorization, and secure data access.\nFamiliarity with cloud-based application deployment.\nExperience working with Git and collaborative software development workflows.\nAbility to analyze search results and identify relevance problems.\nUnderstanding of system performance, latency, and scalability considerations.\nExperience writing maintainable, well-tested production code.\nFamiliarity with logging, monitoring, and production troubleshooting.\nAbility to translate product requirements into technical search functionality.\nStrong analytical and problem-solving skills.\nAbility to collaborate effectively with product managers and engineers.\nAbility to independently own assigned technical projects.\nNice to Have\nExperience building semantic search or hybrid search applications.\nFamiliarity with Elasticsearch, OpenSearch, or comparable search technologies.\nExperience with vector databases such as Pinecone, Weaviate, Qdrant, or pgvector.\nExperience implementing retrieval-augmented generation systems.\nFamiliarity with embedding models and vector similarity techniques.\nExperience with learning-to-rank or neural reranking approaches.\nUnderstanding of BM25 and traditional information retrieval algorithms.\nFamiliarity with search evaluation metrics such as precision, recall, MRR, and NDCG.\nExperience building search relevance evaluation datasets.\nExperience with query rewriting, expansion, or intent classification.\nFamiliarity with large language models and AI-powered search applications.\nExperience with OpenAI, Anthropic, or comparable model APIs.\nExperience building search features for SaaS or e-commerce platforms.\nFamiliarity with product catalogs, merchant data, or operational knowledge systems.\nExperience with TypeScript and Node.js backend development.\nExperience working with AWS or serverless infrastructure.\nFamiliarity with data pipelines and event-driven indexing.\nExperience implementing permission-aware retrieval in multi-tenant applications.\nUnderstanding of healthcare data privacy considerations.\nExperience using AI-assisted development tools such as Cursor or Claude Code.","description_format":"text","description_chars":8814,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Commerce","Health Care","Telemedicine & Virtual Care","Pharmacies"],"lifecycle":[{"event":"open","at":"2026-11-12T19:17:00Z"}],"visa":[{"country":"US","licensed_sponsor":true,"adverts_offering_sponsorship_24m":0,"sanctioned_now":false,"evidence":"H-1B filings in 12 months: 3","filings_12m":3,"filings_prev_12m":2,"green_card_filings_12m":0,"median_offered_wage_usd":94167,"route":null,"cap_exempt":false,"checked_at":"2026-11-15T09:21:55+00:00","sources":["US Department of Labor: LCA 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