About Bask Health
Bask Health is a software platform that enables entrepreneurs, doctors, and healthcare businesses to launch, manage, and grow direct-to-consumer telehealth businesses.
Our platform brings together patient experiences, clinical workflows, pharmacy operations, and commerce tools in one place.
We'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.
Engineering 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
We'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.
Bask 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.
You'll help develop machine learning-powered search and discovery capabilities that improve how information is retrieved, ranked, and presented across our applications.
This 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.
Your 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.
You'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.
You'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.
Because 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.
This 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.
We'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.
What You'll Do
- 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 that combine keyword matching with semantic retrieval.
- Develop ranking and reranking approaches that improve search result relevance.
- Implement query processing, normalization, and understanding capabilities.
- Build search indexes and data pipelines that keep searchable information accurate and up to date.
- Develop APIs that expose 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.
- Develop search functionality that respects user permissions and tenant boundaries.
- Build automated tests for search behavior, retrieval quality, and ranking performance.
- Create evaluation datasets that represent realistic search queries and expected results.
- Measure search relevance using appropriate 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.
- 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.
- Familiarity with Elasticsearch, OpenSearch, or comparable search technologies.
- Experience with vector databases such as Pinecone, Weaviate, Qdrant, or pgvector.
- Experience implementing retrieval-augmented generation systems.
- Familiarity with embedding models and vector similarity techniques.
- Experience with learning-to-rank or neural reranking approaches.
- Understanding of BM25 and traditional information retrieval algorithms.
- Familiarity with search evaluation metrics such as precision, recall, MRR, and NDCG.
- Experience building search relevance evaluation datasets.
- Experience with query rewriting, expansion, or intent classification.
- Familiarity with large language models and AI-powered search applications.
- Experience with OpenAI, Anthropic, or comparable model APIs.
- Experience building search features for SaaS or e-commerce platforms.
- Familiarity with product catalogs, merchant data, or operational knowledge systems.
- Experience with TypeScript and Node.js backend development.
- Experience working with AWS or serverless infrastructure.
- Familiarity with data pipelines and event-driven indexing.
- Experience implementing permission-aware retrieval in multi-tenant applications.
- Understanding of healthcare data privacy considerations.
- Experience using AI-assisted development tools such as Cursor or Claude Code.
