ML Systems Engineer

About [Employer]

[Employer] is a software platform that enables entrepreneurs, doctors, and healthcare businesses to launch, manage, and grow direct-to-consumer telehealth businesses.

Our platform brings together patient experiences, clinical workflows, pharmacy operations, and commerce tools in one place.

We'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.

Engineering at [Employer] 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.

About the Role

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 [Employer]'s platform.

[Employer] 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.

You'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.

This 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.

Your 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 [Employer].

You'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.

Because [Employer] 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.

This 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.

We'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.

What You'll Do

• Design, build, and maintain production infrastructure for machine learning systems across [Employer].
• 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.
• 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.

Originally posted on [Employer]