ML Systems Engineer

Job not on LinkedIn

🔥 11 minutes ago

🇺🇸 United States – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 25%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.Why this score:

• Just posted.
• Jobs at this company usually fill in 60 days.
• 102 open roles for a team of 16.
• No salary listed.

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Bask Health

WebsiteLinkedInAll Job Openings

11 - 50 employees

🏥 Healthcare

💼 Consulting

📦 Logistics

💰 Pre Seed Round on 2022-05

Healthcare • Consulting • Logistics

Bask Health is a robust platform providing a comprehensive suite of customizable telehealth solutions. It offers tools such as a questionnaire builder, patient portals, and pharmacy fulfillment services, all secured with strong encryption and HIPAA-compliance. Bask Health seamlessly integrates telehealth and e-commerce, enabling streamlined patient care from consultation to delivery. The platform supports entrepreneurs, doctors, and developers by offering scalable no-code solutions that enhance user experience and business growth. With its nationwide pharmacy network, Bask Health empowers businesses to efficiently manage prescriptions, patient data, and order fulfillment across the entire digital health landscape.

📋 Description

• Design, build, and maintain production infrastructure for machine learning systems across Bask Health • Develop model-serving services that expose machine learning predictions through reliable APIs • Build and maintain infrastructure for batch and real-time model inference • Implement automated workflows for model packaging, deployment, and version management • Develop reusable deployment patterns for machine learning applications • Build systems for managing model artifacts, configurations, and runtime dependencies • Implement continuous integration and deployment pipelines for ML services • Develop infrastructure that supports reproducible model releases and controlled rollbacks • Build monitoring systems for model-serving latency, throughput, errors, and availability • Implement logging and tracing for production machine learning services • Support model performance monitoring and detection of unexpected changes in input data • Develop data processing and feature preparation pipelines that support production inference • Integrate machine learning services with existing backend applications and databases • Build asynchronous processing systems for large or long-running inference tasks • Optimize inference performance, resource usage, and infrastructure costs • Develop automated tests for model-serving APIs and infrastructure components • Implement safeguards for service failures, timeouts, retries, and fallback behavior • Work with machine learning engineers to define deployment and operational requirements • Collaborate with applied scientists to transition validated models into production • Develop tools that simplify model deployment and troubleshooting for engineering teams • Implement appropriate authentication, authorization, and access controls for ML services • Ensure production systems respect tenant boundaries and approved data-handling requirements • Investigate infrastructure incidents and resolve production reliability issues • Improve system scalability and fault tolerance as application demand grows • Support experiment environments and testing infrastructure for machine learning development • Document infrastructure architecture, deployment procedures, and operational standards • Participate in code reviews, technical planning, and architecture discussions • Own assigned ML infrastructure projects from design through deployment, monitoring, and continuous improvement

🎯 Requirements

• 3+ years of professional experience in software engineering, machine learning engineering, infrastructure engineering, or a closely related technical field • Experience building and maintaining production backend services or infrastructure • Strong programming skills in Python, TypeScript, or comparable languages • Understanding of machine learning development and deployment workflows • Experience developing APIs and integrating backend services • Familiarity with cloud infrastructure and production application deployment • Understanding of distributed systems fundamentals and service reliability • Experience with automated testing and continuous integration practices • Familiarity with containerization or comparable application packaging approaches • Understanding of model inference and production model-serving requirements • Experience working with databases and structured application data • Familiarity with asynchronous processing and background job systems • Experience implementing logging, monitoring, and operational diagnostics • Understanding of system performance, latency, throughput, and scalability • Ability to troubleshoot production infrastructure and application failures • Familiarity with authentication, authorization, and secure service communication • Experience working with Git and collaborative engineering workflows • Understanding of software configuration and dependency management • Ability to design reusable infrastructure components and engineering tools • Familiarity with data processing pipelines and production data quality considerations • Experience writing maintainable, well-tested production code • Understanding of deployment automation and release management • Ability to evaluate technical tradeoffs involving reliability, performance, and cost • Strong analytical and problem-solving skills • Ability to collaborate effectively with machine learning engineers, applied scientists, and software engineers • Nice to have: Experience deploying and operating machine learning models in production • Nice to have: Familiarity with MLOps platforms and model lifecycle management tools • Nice to have: Experience with MLflow, SageMaker, or comparable machine learning infrastructure • Nice to have: Familiarity with model registries and artifact management systems • Nice to have: Experience building real-time inference APIs • Nice to have: Experience with batch inference and distributed data processing • Nice to have: Familiarity with Docker, Kubernetes, or comparable container orchestration technologies • Nice to have: Experience working with AWS Lambda, SQS, EventBridge, or related cloud services • Nice to have: Familiarity with serverless infrastructure and infrastructure-as-code tools • Nice to have: Experience with SST or comparable application deployment frameworks • Nice to have: Experience building CI/CD pipelines for machine learning applications • Nice to have: Familiarity with model monitoring and data drift detection • Nice to have: Experience with feature stores or reusable feature pipelines • Nice to have: Understanding of model versioning, canary deployments, and rollback strategies • Nice to have: Familiarity with GPU-based inference or model-serving optimization • Nice to have: Experience supporting large language model applications or embedding services • Nice to have: Familiarity with vector databases and retrieval infrastructure • Nice to have: Experience building internal developer platforms or reusable infrastructure libraries • Nice to have: Understanding of HIPAA and healthcare data privacy considerations • Nice to have: Experience using AI-assisted development tools such as Cursor or Claude Code

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