{"id":4190827,"url":"https://alion.io/job/bask-health-ml-systems-engineer","title":"ML Systems Engineer","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":"CI/CD","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":"TypeScript","optional":false},{"name":"Amazon EventBridge","optional":true},{"name":"Amazon SageMaker","optional":true},{"name":"AWS","optional":true},{"name":"AWS Lambda","optional":true},{"name":"Docker","optional":true},{"name":"Feature Store","optional":true},{"name":"HIPAA","optional":true},{"name":"Kubernetes","optional":true},{"name":"MLFlow","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 an ML Systems Engineer to build and maintain the infrastructure that allows machine learning models and AI-powered services to operate reliably across Bask Health's platform.\nBask Health brings together patient experiences, merchant operations, clinical workflows, pharmacy coordination, and commerce tools. As machine learning becomes more integrated into these applications, we need dependable systems for preparing data, deploying models, managing inference, monitoring performance, and maintaining production reliability.\nYou'll help develop the engineering infrastructure that connects machine learning development with real-world application use. Your work will support the complete operational lifecycle of machine learning systems, from model packaging and deployment to monitoring, troubleshooting, and ongoing maintenance.\nThis is a hands-on engineering role that combines backend software development, cloud infrastructure, machine learning operations, and distributed systems. You'll work with APIs, data pipelines, deployment environments, model-serving services, and monitoring tools to make machine learning capabilities accessible to other engineering teams.\nYour work may include building model-serving infrastructure, implementing automated deployment pipelines, developing batch and real-time inference services, managing model artifacts, and creating systems that monitor performance and operational health.\nYou'll collaborate with machine learning engineers, applied scientists, backend engineers, and infrastructure specialists to understand model requirements and translate them into reliable production systems. You'll help establish reusable deployment patterns, improve developer workflows, and reduce the complexity of operating machine learning applications.\nBecause Bask operates in healthcare-related environments, you'll also need to consider data privacy, access controls, tenant isolation, and appropriate handling of sensitive information throughout the machine learning lifecycle.\nThis position emphasizes strong software engineering fundamentals and practical infrastructure ownership. You'll contribute to technical design, build production services, investigate operational problems, and continuously improve system performance.\nWe're looking for an engineer who enjoys working at the intersection of machine learning and infrastructure, understands the challenges of running models in production, and wants to build systems that other engineers can depend on.\nWhat You'll Do\nDesign, build, and maintain production infrastructure for machine learning systems across Bask Health.\nDevelop model-serving services that expose machine learning predictions through reliable APIs.\nBuild and maintain infrastructure for batch and real-time model inference.\nImplement automated workflows for model packaging, deployment, and version management.\nDevelop reusable deployment patterns for machine learning applications.\nBuild systems for managing model artifacts, configurations, and runtime dependencies.\nImplement continuous integration and deployment pipelines for ML services.\nDevelop infrastructure that supports reproducible model releases and controlled rollbacks.\nBuild monitoring systems for model-serving latency, throughput, errors, and availability.\nImplement logging and tracing for production machine learning services.\nSupport model performance monitoring and detection of unexpected changes in input data.\nDevelop data processing and feature preparation pipelines that support production inference.\nIntegrate machine learning services with existing backend applications and databases.\nBuild asynchronous processing systems for large or long-running inference tasks.\nOptimize inference performance, resource usage, and infrastructure costs.\nDevelop automated tests for model-serving APIs and infrastructure components.\nImplement safeguards for service failures, timeouts, retries, and fallback behavior.\nWork with machine learning engineers to define deployment and operational requirements.\nCollaborate with applied scientists to transition validated models into production.\nDevelop tools that simplify model deployment and troubleshooting for engineering teams.\nImplement appropriate authentication, authorization, and access controls for ML services.\nEnsure production systems respect tenant boundaries and approved data-handling requirements.\nInvestigate infrastructure incidents and resolve production reliability issues.\nImprove system scalability and fault tolerance as application demand grows.\nSupport experiment environments and testing infrastructure for machine learning development.\nDocument infrastructure architecture, deployment procedures, and operational standards.\nParticipate in code reviews, technical planning, and architecture discussions.\nOwn assigned ML infrastructure projects from design through deployment, monitoring, and continuous improvement.\nRequirements\n3+ years of professional experience in software engineering, machine learning engineering, infrastructure engineering, or a closely related technical field.\nExperience building and maintaining production backend services or infrastructure.\nStrong programming skills in Python, TypeScript, or comparable languages.\nUnderstanding of machine learning development and deployment workflows.\nExperience developing APIs and integrating backend services.\nFamiliarity with cloud infrastructure and production application deployment.\nUnderstanding of distributed systems fundamentals and service reliability.\nExperience with automated testing and continuous integration practices.\nFamiliarity with containerization or comparable application packaging approaches.\nUnderstanding of model inference and production model-serving requirements.\nExperience working with databases and structured application data.\nFamiliarity with asynchronous processing and background job systems.\nExperience implementing logging, monitoring, and operational diagnostics.\nUnderstanding of system performance, latency, throughput, and scalability.\nAbility to troubleshoot production infrastructure and application failures.\nFamiliarity with authentication, authorization, and secure service communication.\nExperience working with Git and collaborative engineering workflows.\nUnderstanding of software configuration and dependency management.\nAbility to design reusable infrastructure components and engineering tools.\nFamiliarity with data processing pipelines and production data quality considerations.\nExperience writing maintainable, well-tested production code.\nUnderstanding of deployment automation and release management.\nAbility to evaluate technical tradeoffs involving reliability, performance, and cost.\nStrong analytical and problem-solving skills.\nAbility to collaborate effectively with machine learning engineers, applied scientists, and software engineers.\nNice to Have\nExperience deploying and operating machine learning models in production.\nFamiliarity with MLOps platforms and model lifecycle management tools.\nExperience with MLflow, SageMaker, or comparable machine learning infrastructure.\nFamiliarity with model registries and artifact management systems.\nExperience building real-time inference APIs.\nExperience with batch inference and distributed data processing.\nFamiliarity with Docker, Kubernetes, or comparable container orchestration technologies.\nExperience working with AWS Lambda, SQS, EventBridge, or related cloud services.\nFamiliarity with serverless infrastructure and infrastructure-as-code tools.\nExperience with SST or comparable application deployment frameworks.\nExperience building CI/CD pipelines for machine learning applications.\nFamiliarity with model monitoring and data drift detection.\nExperience with feature stores or reusable feature pipelines.\nUnderstanding of model versioning, canary deployments, and rollback strategies.\nFamiliarity with GPU-based inference or model-serving optimization.\nExperience supporting large language model applications or embedding services.\nFamiliarity with vector databases and retrieval infrastructure.\nExperience building internal developer platforms or reusable infrastructure libraries.\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":9161,"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":["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: 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