Senior Machine Learning OPS

Inovalon was founded in 1998 on the belief that technology, and data specifically, would empower the transformation of the entire healthcare ecosystem for the better, improving both outcomes and economics. At Inovalon, we believe that when our customers are successful in their missions, healthcare improves. Therefore, we focus on empowering them with data-driven solutions. And the momentum is building.

Together, as ONE Inovalon, we are a united force delivering solutions that address healthcare's greatest needs. Through our mission-based culture of inclusion and innovation, our organization brings value not just to our customers, but to the millions of patients and members they serve.

Inovalon is a leading healthcare technology company dedicated to revolutionizing the healthcare industry through innovative AI and machine learning solutions. Our mission is to leverage cutting‑edge technology to improve health outcomes and streamline healthcare processes. We are seeking a highly skilled Senior Machine Learning SDET to ensure the quality, reliability, and safety of our ML‑powered healthcare products.

As a Senior Machine Learning SDET, you will be responsible for designing, implementing, and maintaining robust testing frameworks and quality strategies for machine learning systems across their entire lifecycle. You will collaborate closely with data scientists, ML engineers, software engineers, and product managers to validate models, data pipelines, and ML‑driven services in production environments. Your work will help ensure that our ML solutions are accurate, performant, secure, and compliant with healthcare standards, ultimately improving patient care and operational efficiency.

• Test Strategy for ML Systems:
  Define and own the end‑to‑end test strategy for ML models, data pipelines, and services, including functional, performance, regression, and reliability testing.
• ML Model Validation:
  Design automated tests to validate model behavior (e.g., accuracy, drift detection, bias, stability, and robustness) across training, staging, and production environments.
• Data Quality & Pipeline Testing:
  Build automated tests and monitoring for data pipelines to ensure schema integrity, data completeness, data freshness, and correctness for training and inference.
• Test Automation Frameworks:
  Develop and maintain scalable test automation frameworks and tools for APIs, microservices, and ML workflows, integrating them into CI/CD pipelines.
• Performance & Scalability:
  Design and execute load and performance tests for ML inference services and batch jobs, focusing on latency, throughput, and resource utilization.
• Monitoring & Observability:
  Collaborate with ML and platform engineers to implement monitoring, logging, and alerting for ML systems, including model performance and data drift.
• Shift‑Left Quality:
  Embed quality practices early in the ML development lifecycle, including testable model design, test data generation, and automated validation in CI.
• Tooling & Infrastructure:
  Leverage and extend existing tools (e.g., Python test frameworks, cloud services, containerization, big data tools) to support robust ML testing.
• Collaboration:
  
  Work closely with cross‑functional teams to understand business and regulatory requirements and translate them into verifiable test scenarios and acceptance criteria.
• Mentorship:
  Provide guidance and mentorship to engineers on best practices for testing ML and data‑intensive systems, fostering a culture of quality and continuous improvement.

• Education:
  
  Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field;
  Ph.D. is a plus.
• Experience:
  
  Minimum of 5 years of experience in software test engineering or SDET roles, with at least 3 years focused on testing machine learning, data, or distributed backend systems in production.

• Strong proficiency in Python and testing frameworks such as pytest, unittest, or similar, with experience testing ML code and data pipelines.
• Hands‑on experience with ML libraries and ecosystems (e.g., Tensor Flow, PyTorch, scikit‑learn) sufficient to design model…