À propos
Key Responsibilities
Define and own end‑to‑end test strategy for ML models, data pipelines, and services, including functional, performance, regression, and reliability testing.
Design automated tests to validate model behavior (accuracy, drift detection, bias, stability, and robustness) across training, staging, and production environments.
Build automated tests and monitoring for data pipelines to ensure schema integrity, data completeness, data freshness, and correctness for training and inference.
Develop and maintain scalable test automation frameworks and tools for APIs, microservices, and ML workflows, integrating them into CI/CD pipelines.
Design and execute load and performance tests for ML inference services and batch jobs, focusing on latency, throughput, and resource utilization.
Collaborate with ML and platform engineers to implement monitoring, logging, and alerting for ML systems, including model performance and data drift.
Embed quality practices early in the ML development lifecycle, including testable model design, test data generation, and automated validation in CI.
Leverage and extend existing tools (Python test frameworks, cloud services, containerization, big data tools) to support robust ML testing.
Work closely with cross‑functional teams to understand business and regulatory requirements and translate them into verifiable test scenarios and acceptance criteria.
Provide guidance and mentorship to engineers on best practices for testing ML and data‑intensive systems, fostering a culture of quality and continuous improvement.
Qualifications
Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or related field; Ph.D. is a plus.
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.
Technical Skills
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 (TensorFlow, PyTorch, scikit‑learn) to design model validation and testing strategies.
Hands‑on experience with generative AI concepts (LLMs, RAG, vector databases, prompt engineering, transfer learning) and their testing implications is a strong plus.
Experience with cloud platforms (AWS, GCP, or Azure) and containerization/orchestration (Docker, Kubernetes) for deploying and testing ML services.
Strong knowledge of data structures, algorithms, and software engineering best practices, including code reviews, version control, and CI/CD.
Experience building and integrating automated tests for REST/gRPC APIs and microservices, including contract and integration testing.
Experience with performance and load testing tools and techniques for backend and ML inference services.
Experience with observability and monitoring tools (Prometheus, Grafana, ELK, or cloud‑native equivalents) for tracking model and system health.
Domain Knowledge
Understanding of healthcare data standards (HL7, FHIR) and regulations (HIPAA) is a plus.
Experience testing systems in regulated, high‑compliance, or safety‑critical environments is highly desirable.
Soft Skills
Excellent problem‑solving and analytical skills, with strong focus on edge cases, failure modes, and risk assessment.
Strong communication and collaboration abilities, comfortable working across ML, data, product, and platform teams.
Ability to work independently and as part of a team in a fast‑paced environment, balancing quality, speed, and pragmatism.
Benefits & What We Offer
Competitive salary and benefits package.
Opportunity to work on impactful ML‑driven healthcare products that improve patient outcomes and operational efficiency.
Collaborative and innovative work environment.
Professional development and growth opportunities in ML quality engineering and SDET leadership.
Flexible work arrangements, including hybrid and remote options.
Equity and Inclusion Statement Inovalon is a proud equal‑opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirement.
By embracing inclusion, we enhance our work environment and drive business success. Inovalon strives to provide equal opportunities to the communities where we operate and to our clients and everyone whom we serve. We endeavor to create a culture of inclusion in which our associates feel empowered to bring their full, authentic selves to work and pursue their professional goals in an equitable setting.
This position is not eligible for immigration sponsorship (e.g. H‑1B, TN, or E‑3). Applicants must be authorized to work in the United States as a condition of employment. (This is only applicable for US‑based positions).
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Compétences linguistiques
- English
Avis aux utilisateurs
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