About The Role

Join our team as a Software Development Engineer in Test (ML/AI). In this role, you will specialize in ML/AI quality, automation for model evaluation, and validation of AI-powered workflows. You will guide quality strategy, develop automation frameworks, and manage implementation for complex, cross-functional, machine learning-powered products and services. You will work closely with ML, engineering, product, and infrastructure teams, and shape how quality is built, monitored, and expanded.

Definir y completar estrategias de calidad, planes de prueba y cobertura de automatización para servicios y componentes de plataforma impulsados por ML.
Utilizar LLMs y otras técnicas asistidas por IA para generar, expandir y mantener casos de prueba de alto valor para flujos de trabajo impulsados por ML.
Diseñar, desarrollar y mantener marcos de automatización escalables para servicios backend, API y sistemas de inferencia ML utilizando Python y/o Java.
Experience with cloud and container technologies (AWS, GCP, Kubernetes, Docker)
Hands-on experience with automation frameworks such as pytest, JUnit, Selenium, Playwright, Cypress, or Appium
Strong understanding of SDLC, Agile methodologies, and release processes
Strong experience testing RESTful APIs, microservices, and distributed architectures
Experience with CI/CD systems and test pipelines (Jenkins, GitHub Actions, etc.)
Experience using LLMs to generate, transform, and prioritize test cases for AI-powered experiences
Familiarity with databases, monitoring, and observability tools
Proficiency in Python, Java, JS or similar languages for automation development
Excellent problem-solving, debugging, and communication skills
3+ years of experience as an SDET or QE engineer focused on backend and distributed systems
Experience with AI evaluation tooling, prompt evaluation frameworks, model monitoring, or human-in-the-loop review workflows
Bachelor’s degree in Computer Science or equivalent practical experience
Experience validating ML outputs using statistical analysis or scenario-based testing approaches
Prior work in content moderation ML, security, fraud detection, or adversarial ML
Familiarity with ML infrastructure, data pipelines, or model-serving platforms (Seldon, KServe, Ray Serve, etc.)
Experience testing high-scale, low-latency online services
Experience with Databricks or similar ML platform tooling
Experience testing mobile, console, or other non-PC platforms
Familiarity with Node.js, React, or modern frontend technologies
Strong combination of automation engineering and delivery ownership
Ability to drive quality across complex cross-functional initiatives
Practical understanding of how to test non-deterministic AI systems and separate model variance from quality regressions
Ability to influence engineering teams and promote quality guidelines
Proven risk management and dependency coordination skills
Passion for scalable, reliable, and maintainable automation systems

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