Machine Learning Engineer

Interwell Health is seeking a Machine Learning Engineer to lead end-to-end ML development, MLOps, deployment, and monitoring. The role covers traditional ML work as well as capabilities built on large language models, with remote work as the preferred arrangement.

Location and Work Model

Remote

Responsibilities

• Design and deliver complete machine learning solutions, defining technical requirements, building scalable architectures, and establishing monitoring, logging, and maintenance workflows.
• Collaborate with engineers, product managers, clinicians, and cross-functional partners to create new ML products and improve existing systems.
• Own the design and implementation of MLOps frameworks, including pipelines, CI/CD integration, drift detection, retraining workflows, and rollback strategies.
• Monitor production model performance, diagnose issues, propose remediation steps, and maintain robust test coverage and system reliability.
• Apply modern software engineering practices to deliver scalable, secure, and maintainable AI/ML systems.
• Develop and tailor API integrations to enable seamless connectivity between cloud-based systems and ML services.
• Contribute to architectural discussions to ensure ML platforms meet compliance, performance, and scalability standards.

Requirements

• Bachelor's degree in Computer Science, Data Analytics, Software or Computer Engineering, Computational Statistics, Mathematics, or a related field.
• Three or more years of end-to-end ML development in production, including data preparation, feature engineering, modeling, calibration, deployment, monitoring, and maintenance.
• Three or more years of MLOps experience building production pipelines (CI/CD, model registry, feature store), implementing monitoring and drift detection, and automating retraining.
• Three or more years of Python for production ML (testing, packaging, type hints, linting) and SQL for analytical and production workloads; familiarity with Scala is a plus.
• Two or more years working with distributed compute and cloud ML environments (for example Spark/Databricks on Azure, AWS, or GCP) and modern data ecosystems (data lakes, DBMS).
• Strong debugging and optimization skills across data and ML workflows.
• A proven track record of ownership and problem solving, delivering measurable impact and quality under evolving requirements.
• Ability to communicate technical decisions clearly and contribute to documentation and design discussions.
• Demonstrated system design and architecture skills for scalable, high‑performance ML services and batch/streaming workflows; familiarity with API design and service integration patterns.
• Understanding of tradeoffs among latency, cost, performance, and compliance.

Technologies

• Python
• SQL
• Scala
• Spark
• Databricks
• Azure
• AWS
• GCP

Preferred Qualifications

• One year or more of Databricks experience, plus some exposure to infrastructure or networking.
• One year or more implementing LLM based solutions in production, including prompt design, evaluation frameworks, guardrails and cost optimization.
• One year or more designing compliant ML platforms with HIPAA, SOC 2 considerations, PHI/PII governance, access controls, and auditability.