[Hiring] ML Systems Engineer @Bask Health

Role Description

We'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.

• Develop the engineering infrastructure that connects machine learning development with real-world application use.
• Support the complete operational lifecycle of machine learning systems, from model packaging and deployment to monitoring, troubleshooting, and ongoing maintenance.
• Hands-on engineering role combining backend software development, cloud infrastructure, machine learning operations, and distributed systems.
• Work with APIs, data pipelines, deployment environments, model-serving services, and monitoring tools.
• Collaborate with machine learning engineers, applied scientists, backend engineers, and infrastructure specialists.
• Consider data privacy, access controls, tenant isolation, and handling of sensitive information throughout the machine learning lifecycle.
• Contribute to technical design, build production services, investigate operational problems, and continuously improve system performance.

What You'll Do

• 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.

Qualifications

• 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.
• 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.