[Hiring] Senior Machine Learning Engineer @Bask Health

Role Description

We'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. This is a hands-on engineering role focused on making information easier to find, experiences more relevant, and complex workflows more intuitive.

• Work with machine learning models, information retrieval systems, embeddings, ranking algorithms, and production application infrastructure.
• Help develop the underlying intelligence that allows applications to surface useful information, prioritize relevant results, and adapt experiences to different user needs.
• Consider privacy, access controls, and the limitations of personalized recommendations in healthcare-related environments.
• Combine strong machine learning fundamentals with practical software engineering skills.

What You'll Do

• Design, build, and deploy machine learning systems for search, discovery, ranking, and personalization.
• Develop semantic search capabilities that improve how users find information across Bask Health's applications.
• Build and optimize retrieval pipelines using embeddings, vector search, and traditional search methods.
• Design hybrid search systems combining lexical matching, semantic retrieval, and relevance ranking.
• Develop ranking models and algorithms that prioritize useful search results.
• Build recommendation systems that surface relevant platform features, resources, or workflow information.
• Develop contextual personalization capabilities based on approved user and application signals.
• Work with product teams to identify opportunities to improve search relevance and information discovery.
• Build scalable indexing, retrieval, and search-serving infrastructure.
• Design data pipelines that support search models, ranking systems, and recommendation features.
• Develop approaches for generating, storing, updating, and evaluating embeddings.
• Experiment with learning-to-rank methods and machine learning-based relevance improvements.
• Implement query understanding, query expansion, and search intent interpretation where appropriate.
• Improve search performance across structured and unstructured data sources.
• Collaborate with applied AI engineers to support retrieval-augmented generation applications.
• Develop retrieval services that can be reused across patient, merchant, and operational applications.
• Define and track search quality metrics, including relevance, recall, precision, and ranking performance.
• Design offline evaluation datasets and benchmarking procedures for search and recommendation systems.
• Collaborate with product teams to conduct controlled experiments and measure user experience improvements.
• Optimize search latency, throughput, infrastructure utilization, and operational costs.
• Implement monitoring and observability for production search and personalization services.
• Investigate search quality issues, indexing failures, and ranking inconsistencies.
• Ensure retrieval and personalization systems respect user permissions and data access boundaries.
• Design appropriate safeguards for sensitive healthcare-related recommendations.
• Work with backend and frontend engineers to integrate search and recommendation features into production applications.
• Contribute to architecture decisions involving search infrastructure, databases, and machine learning services.
• Mentor engineers and share expertise in information retrieval, ranking, and production ML engineering.
• Own search and personalization initiatives from technical design through deployment, measurement, and ongoing improvement.

Qualifications

• 5+ years of professional experience in machine learning engineering, software engineering, search engineering, or a closely related technical discipline.
• Demonstrated experience building and deploying machine learning systems in production.
• Strong programming skills in Python and experience developing production software services.
• Experience building search, recommendation, ranking, or information retrieval systems.
• Strong understanding of machine learning fundamentals and applied statistical methods.
• Familiarity with modern information retrieval techniques and search relevance optimization.
• Experience with embeddings, vector representations, or semantic similarity methods.
• Understanding of lexical search, semantic search, and hybrid retrieval approaches.
• Experience designing or integrating scalable search infrastructure.
• Strong understanding of ranking algorithms and relevance evaluation.
• Experience working with structured and unstructured datasets.
• Familiarity with data processing, feature engineering, and machine learning pipelines.
• Experience building backend APIs and integrating machine learning services into applications.
• Understanding of database design, indexing, and query performance.
• Experience evaluating model or search system performance using quantitative metrics.
• Familiarity with offline evaluation, experimentation, and performance benchmarking.
• Experience optimizing production systems for latency, scalability, and reliability.
• Understanding of cloud infrastructure and deployment practices.
• Familiarity with monitoring, logging, and troubleshooting production services.
• Strong software engineering practices, including testing, code review, and version control.
• Understanding of data privacy, access controls, and secure application design.
• Ability to translate product requirements into practical machine learning solutions.
• Experience collaborating with product managers and cross-functional engineering teams.
• Ability to independently own complex engineering projects from design through deployment.
• Strong analytical thinking, communication skills, and technical problem-solving abilities.

Nice to Have

• Experience building personalized recommendation systems for SaaS or consumer applications.
• Familiarity with learning-to-rank models and neural ranking approaches.
• Experience with approximate nearest neighbor search and vector indexing techniques.
• Familiarity with Elasticsearch, OpenSearch, Vespa, or comparable search infrastructure.
• Experience using vector databases such as Pinecone, Weaviate, Milvus, or pgvector.
• Experience with embedding models and transformer-based retrieval systems.
• Familiarity with cross-encoders, reranking models, and multi-stage retrieval pipelines.
• Experience developing query understanding or natural language search features.
• Experience building retrieval systems for large language model applications.
• Familiarity with retrieval-augmented generation architectures.
• Experience with recommendation approaches such as collaborative filtering or content-based ranking.
• Experience with online experimentation, A/B testing, and search relevance measurement.
• Familiarity with clickstream analysis and user behavior modeling.
• Experience developing real-time or near-real-time personalization systems.
• Experience with large-scale data processing frameworks or distributed computing.
• Familiarity with AWS and cloud-based machine learning infrastructure.
• Experience building search or recommendation features for e-commerce platforms.
• Experience with healthcare, telehealth, or other privacy-sensitive software.
• Understanding of HIPAA and healthcare data privacy considerations.
• Experience using AI-assisted development tools such as Cursor or Claude Code.