{"id":4190823,"url":"https://alion.io/job/bask-health-machine-learning-engineer-personalization","title":"Machine Learning Engineer, Personalization","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":"A/B Testing","optional":false},{"name":"Claude Code","optional":false},{"name":"Cursor","optional":false},{"name":"Git","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Recommender Systems","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"Amazon SageMaker","optional":true},{"name":"AWS","optional":true},{"name":"Embeddings","optional":true},{"name":"Feature Store","optional":true},{"name":"HIPAA","optional":true},{"name":"JavaScript","optional":true},{"name":"Next.js","optional":true},{"name":"Node JS","optional":true},{"name":"React.js","optional":true},{"name":"Reinforcement Learning","optional":true},{"name":"TypeScript","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, Personalization to build and improve intelligent systems that help Bask Health deliver more relevant and personalized experiences across our platform.\nBask Health provides the technology behind direct-to-consumer telehealth businesses, bringing together merchant storefronts, patient experiences, clinical workflows, pharmacy operations, and commerce capabilities. As merchants grow, we want to help them create digital experiences that are responsive to user needs, preferences, and interactions.\nYou'll develop machine learning models and recommendation systems that support personalized content, relevant platform experiences, and intelligent discovery within approved product workflows.\nThis is a hands-on machine learning engineering role that combines model development, data processing, backend integration, experimentation, and production monitoring. You'll work with application data, user interaction signals, machine learning algorithms, and platform services to build personalization capabilities that operate reliably at scale.\nYour work may include developing ranking models, recommendation algorithms, user preference representations, behavioral feature pipelines, and systems that adapt application experiences based on relevant, authorized signals.\nYou'll collaborate with product managers, backend engineers, frontend engineers, data specialists, and other machine learning engineers to identify personalization opportunities and translate them into production-ready features.\nYou'll also help design experiments to measure whether personalization improves user experience, engagement, and relevant product outcomes. You'll evaluate model quality, investigate performance issues, and continuously improve algorithms based on measured results.\nBecause Bask operates in healthcare-related environments, personalization must respect privacy, data access permissions, and clinical boundaries. You'll ensure that models do not independently determine medical eligibility, recommend treatments, change prescriptions, or override approved clinical workflows.\nThis position emphasizes practical machine learning, strong software engineering fundamentals, and measurable product improvements. You'll contribute to model design, implement production systems, and own features through deployment and ongoing optimization.\nWe're looking for an engineer who enjoys working with recommendation systems, understands the challenges of real-world personalization, and wants to build intelligent product experiences that are useful, reliable, and responsible.\nWhat You'll Do\nDesign, develop, and deploy machine learning models that support personalized experiences across Bask Health.\nBuild recommendation systems for approved platform content and user experiences.\nDevelop ranking algorithms that improve the relevance of information presented to users.\nImplement personalization models using behavioral, contextual, and preference-related signals.\nBuild feature engineering pipelines that transform application data into model-ready inputs.\nDevelop systems for representing user preferences and interaction patterns.\nImplement personalized content ordering and discovery capabilities within approved workflows.\nBuild backend services and APIs that deliver personalization results to applications.\nCollaborate with frontend engineers to integrate personalization into user-facing experiences.\nDevelop baseline models and compare them against more advanced personalization approaches.\nExperiment with collaborative filtering, content-based recommendation, and learning-to-rank methods.\nImplement model training, validation, and evaluation workflows.\nDevelop offline evaluation methods for recommendation and ranking quality.\nSupport online experimentation and A/B testing for personalization features.\nAnalyze engagement and interaction data to identify opportunities for model improvements.\nMonitor production model performance, latency, and reliability.\nInvestigate changes in recommendation quality and unexpected model behavior.\nDevelop strategies for handling sparse data and new-user cold-start scenarios.\nImplement appropriate fallbacks when personalization signals are unavailable.\nOptimize model inference speed and resource usage.\nCollaborate with backend engineers to support scalable model serving.\nWork with product managers to define personalization goals and success metrics.\nEvaluate model performance across relevant user groups and application contexts.\nImplement privacy-conscious data processing and feature access controls.\nEnsure personalization features respect tenant boundaries and approved platform permissions.\nDocument model assumptions, evaluation results, and known limitations.\nParticipate in code reviews, technical planning, and architecture discussions.\nOwn assigned personalization features from model development through deployment, monitoring, and continuous improvement.\nRequirements\n3+ years of professional experience in machine learning engineering, applied machine learning, software engineering, or a closely related technical field.\nExperience developing and deploying machine learning models in production.\nStrong programming skills in Python or comparable machine learning development languages.\nUnderstanding of recommendation systems, ranking models, or predictive modeling.\nExperience working with machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn.\nFamiliarity with supervised learning and common model evaluation techniques.\nExperience developing data processing and feature engineering pipelines.\nStrong understanding of SQL and structured application data.\nFamiliarity with statistical analysis and experimental design.\nExperience integrating machine learning models into production applications.\nUnderstanding of backend services and API development.\nFamiliarity with cloud-based machine learning deployment.\nExperience evaluating model quality using appropriate offline metrics.\nUnderstanding of model overfitting, data leakage, and generalization.\nFamiliarity with model training, validation, and testing workflows.\nExperience troubleshooting machine learning performance issues.\nUnderstanding of data quality and feature reliability.\nFamiliarity with model monitoring and production observability.\nExperience writing maintainable, tested production code.\nUnderstanding of authentication, authorization, and secure data handling.\nExperience using Git and collaborative engineering workflows.\nAbility to translate product requirements into machine learning solutions.\nStrong analytical and problem-solving skills.\nAbility to collaborate effectively with product managers, data specialists, and software engineers.\nAbility to independently own machine learning projects from development through deployment.\nNice to Have\nExperience building recommendation systems for e-commerce or SaaS platforms.\nFamiliarity with collaborative filtering and content-based recommendation algorithms.\nExperience with learning-to-rank techniques and ranking optimization.\nUnderstanding of embeddings and vector similarity methods.\nExperience building personalized search or content discovery features.\nFamiliarity with user behavior modeling and preference prediction.\nExperience developing real-time personalization systems.\nUnderstanding of contextual bandits or reinforcement learning approaches to recommendations.\nExperience with A/B testing and online experimentation platforms.\nFamiliarity with recommendation metrics such as NDCG, MAP, precision@k, and recall@k.\nExperience working with sparse interaction data and cold-start problems.\nFamiliarity with feature stores and machine learning data pipelines.\nExperience with AWS SageMaker or comparable ML platform services.\nExperience building low-latency model inference services.\nFamiliarity with TypeScript and Node.js backend development.\nExperience with React or Next.js application integrations.\nFamiliarity with multi-tenant SaaS architecture.\nExperience applying personalization in healthcare-related software while respecting clinical boundaries.\nUnderstanding of HIPAA and healthcare data privacy considerations.\nExperience using AI-assisted development tools such as Cursor or Claude Code.","description_format":"text","description_chars":9084,"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 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