ML Systems Engineer

Bask Health

About The Position

Bask Health is seeking an ML Systems Engineer to build and maintain reliable infrastructure for machine learning models and AI-powered services across its telehealth platform. The role connects machine learning development with production applications and supports the full operational lifecycle of ML systems, including data preparation, model packaging and deployment, inference, monitoring, troubleshooting, and ongoing maintenance. This hands-on engineering position combines backend software development, cloud infrastructure, machine learning operations, and distributed systems. The engineer will build model-serving services, APIs, data pipelines, automated deployment workflows, and monitoring systems, while helping make ML capabilities accessible to other engineering teams. The role also involves establishing reusable deployment patterns, improving developer workflows, and addressing production performance, scalability, reliability, and infrastructure costs. The engineer will collaborate with machine learning engineers, applied scientists, backend engineers, and infrastructure specialists to translate model requirements into dependable production systems. As Bask operates in healthcare-related environments, the role requires attention to data privacy, access controls, tenant isolation, and appropriate handling of sensitive information. The engineer will contribute to technical design, build production services, investigate operational issues, and own assigned ML infrastructure projects from design through deployment and continuous improvement.

Requirements

At least 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 Haves

Experience deploying and operating machine learning models in production.
Familiarity with MLOps platforms and model lifecycle management tools.
Experience with MLflow, SageMaker, or comparable machine learning infrastructure.
Familiarity with model registries and artifact-management systems.
Experience building real-time inference APIs.
Experience with batch inference and distributed data processing.
Familiarity with Docker, Kubernetes, or comparable container-orchestration technologies.
Experience working with AWS Lambda, SQS, EventBridge, or related cloud services.
Familiarity with serverless infrastructure and infrastructure-as-code tools.
Experience with SST or comparable application-deployment frameworks.
Experience building CI/CD pipelines for machine learning applications.
Familiarity with model monitoring and data-drift detection.
Experience with feature stores or reusable feature pipelines.
Understanding of model versioning, canary deployments, and rollback strategies.
Familiarity with GPU-based inference or model-serving optimization.
Experience supporting large language model applications or embedding services.
Familiarity with vector databases and retrieval infrastructure.
Experience building internal developer platforms or reusable infrastructure libraries.
Understanding of HIPAA and healthcare data privacy considerations.
Experience using AI-assisted development tools such as Cursor or Claude Code.

Responsibilities

Design, build, and maintain production infrastructure for machine learning systems.
Develop model-serving services that expose 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.
Support reproducible model releases and controlled rollbacks.
Build monitoring for model-serving latency, throughput, errors, and availability.
Implement logging and tracing for production ML services.
Support model performance monitoring and detection of unexpected changes in input data.
Develop data-processing and feature-preparation pipelines for production inference.
Integrate ML 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 ML engineers to define deployment and operational requirements, and collaborate with applied scientists to transition validated models into production.
Develop tools that simplify model deployment and troubleshooting for engineering teams.
Implement 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 ML 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.