{"id":4146083,"url":"https://alion.io/job/bask-health-senior-machine-learning-engineer-search-personalization","title":"Senior Machine Learning Engineer, Search & Personalization","company":{"id":66537,"name":"Bask Health","domain":"bask.health","url":"https://alion.io/company/bask-health","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"A","score":91,"open_postings":21,"ghost_share":0,"stale_share":0.143,"repost_share":0,"time_to_fill_p50_days":171,"computed_at":"2026-11-12T10:38:37Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","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":"Hybrid Search","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Recommender Systems","optional":false},{"name":"Semantic Search","optional":false},{"name":"A/B Testing","optional":true},{"name":"AWS","optional":true},{"name":"ElasticSearch","optional":true},{"name":"HIPAA","optional":true},{"name":"Milvus","optional":true},{"name":"OpenSearch","optional":true},{"name":"pgvector","optional":true},{"name":"Pinecone","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Reranking","optional":true},{"name":"Vespa","optional":true},{"name":"Weaviate","optional":true}],"status":"live","first_seen_at":"2026-11-12T07:11:28Z","employer_posted_date":"2026-11-12","last_verified_at":"2026-11-12T07:11:28Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"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.About the Role\nWe're looking for a Senior Machine Learning Engineer, Search & Personalization to design, build, and improve intelligent search, discovery, and personalization capabilities across Bask Health's platform.\nThis is a hands-on engineering role focused on making information easier to find, experiences more relevant, and complex workflows more intuitive. You'll work with machine learning models, information retrieval systems, embeddings, ranking algorithms, and production application infrastructure to build search and recommendation experiences that deliver measurable value.\nBask Health supports multiple interconnected applications, including merchant-facing tools, patient experiences, clinical workflows, pharmacy operations, and commerce systems. You'll help develop the underlying intelligence that allows these applications to surface useful information, prioritize relevant results, and adapt experiences to different user needs.\nYour work may include semantic search, hybrid retrieval, intelligent ranking, contextual recommendations, personalized content discovery, and retrieval services that support AI-powered applications.\nYou'll be responsible for more than model experimentation. You'll design and deploy production systems, develop data pipelines, establish evaluation methods, monitor performance, and improve search relevance through ongoing testing and iteration.\nYou'll collaborate closely with product managers, backend engineers, frontend engineers, and applied AI teams to understand how users interact with the platform and where better discovery or personalization can reduce friction.\nBecause Bask operates in healthcare-related environments, you'll also need to consider privacy, access controls, and the limitations of personalized recommendations. Systems that influence sensitive healthcare workflows must follow approved product and clinical boundaries rather than independently making medical decisions.\nWe're looking for someone who combines strong machine learning fundamentals with practical software engineering skills and enjoys building intelligent systems that people use every day.\nWhat You'll Do\nDesign, build, and deploy machine learning systems for search, discovery, ranking, and personalization.\nDevelop semantic search capabilities that improve how users find information across Bask Health's applications.\nBuild and optimize retrieval pipelines using embeddings, vector search, and traditional search methods.\nDesign hybrid search systems combining lexical matching, semantic retrieval, and relevance ranking.\nDevelop ranking models and algorithms that prioritize useful search results.\nBuild recommendation systems that surface relevant platform features, resources, or workflow information.\nDevelop contextual personalization capabilities based on approved user and application signals.\nWork with product teams to identify opportunities to improve search relevance and information discovery.\nBuild scalable indexing, retrieval, and search-serving infrastructure.\nDesign data pipelines that support search models, ranking systems, and recommendation features.\nDevelop approaches for generating, storing, updating, and evaluating embeddings.\nExperiment with learning-to-rank methods and machine learning-based relevance improvements.\nImplement query understanding, query expansion, and search intent interpretation where appropriate.\nImprove search performance across structured and unstructured data sources.\nCollaborate with applied AI engineers to support retrieval-augmented generation applications.\nDevelop retrieval services that can be reused across patient, merchant, and operational applications.\nDefine and track search quality metrics, including relevance, recall, precision, and ranking performance.\nDesign offline evaluation datasets and benchmarking procedures for search and recommendation systems.\nCollaborate with product teams to conduct controlled experiments and measure user experience improvements.\nOptimize search latency, throughput, infrastructure utilization, and operational costs.\nImplement monitoring and observability for production search and personalization services.\nInvestigate search quality issues, indexing failures, and ranking inconsistencies.\nEnsure retrieval and personalization systems respect user permissions and data access boundaries.\nDesign appropriate safeguards for sensitive healthcare-related recommendations.\nWork with backend and frontend engineers to integrate search and recommendation features into production applications.\nContribute to architecture decisions involving search infrastructure, databases, and machine learning services.\nMentor engineers and share expertise in information retrieval, ranking, and production ML engineering.\nOwn search and personalization initiatives from technical design through deployment, measurement, and ongoing improvement.\nRequirements\n5+ years of professional experience in machine learning engineering, software engineering, search engineering, or a closely related technical discipline.\nDemonstrated experience building and deploying machine learning systems in production.\nStrong programming skills in Python and experience developing production software services.\nExperience building search, recommendation, ranking, or information retrieval systems.\nStrong understanding of machine learning fundamentals and applied statistical methods.\nFamiliarity with modern information retrieval techniques and search relevance optimization.\nExperience with embeddings, vector representations, or semantic similarity methods.\nUnderstanding of lexical search, semantic search, and hybrid retrieval approaches.\nExperience designing or integrating scalable search infrastructure.\nStrong understanding of ranking algorithms and relevance evaluation.\nExperience working with structured and unstructured datasets.\nFamiliarity with data processing, feature engineering, and machine learning pipelines.\nExperience building backend APIs and integrating machine learning services into applications.\nUnderstanding of database design, indexing, and query performance.\nExperience evaluating model or search system performance using quantitative metrics.\nFamiliarity with offline evaluation, experimentation, and performance benchmarking.\nExperience optimizing production systems for latency, scalability, and reliability.\nUnderstanding of cloud infrastructure and deployment practices.\nFamiliarity with monitoring, logging, and troubleshooting production services.\nStrong software engineering practices, including testing, code review, and version control.\nUnderstanding of data privacy, access controls, and secure application design.\nAbility to translate product requirements into practical machine learning solutions.\nExperience collaborating with product managers and cross-functional engineering teams.\nAbility to independently own complex engineering projects from design through deployment.\nStrong analytical thinking, communication skills, and technical problem-solving abilities.\nNice to Have\nExperience building personalized recommendation systems for SaaS or consumer applications.\nFamiliarity with learning-to-rank models and neural ranking approaches.\nExperience with approximate nearest neighbor search and vector indexing techniques.\nFamiliarity with Elasticsearch, OpenSearch, Vespa, or comparable search infrastructure.\nExperience using vector databases such as Pinecone, Weaviate, Milvus, or pgvector.\nExperience with embedding models and transformer-based retrieval systems.\nFamiliarity with cross-encoders, reranking models, and multi-stage retrieval pipelines.\nExperience developing query understanding or natural language search features.\nExperience building retrieval systems for large language model applications.\nFamiliarity with retrieval-augmented generation architectures.\nExperience with recommendation approaches such as collaborative filtering or content-based ranking.\nExperience with online experimentation, A/B testing, and search relevance measurement.\nFamiliarity with clickstream analysis and user behavior modeling.\nExperience developing real-time or near-real-time personalization systems.\nExperience with large-scale data processing frameworks or distributed computing.\nFamiliarity with AWS and cloud-based machine learning infrastructure.\nExperience building search or recommendation features for e-commerce platforms.\nExperience with healthcare, telehealth, or other privacy-sensitive software.\nUnderstanding of HIPAA and healthcare data privacy considerations.\nExperience using AI-assisted development tools such as Cursor or Claude 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