Software Development Engineer in Test (SDET)

What we do:

• Zefr is the leader in AI-powered content classifications for brands and advertisers. Zefr’s platform is purpose built for multi-modal content understanding on open platforms like YouTube, TikTok, Meta and Snap, with pre-bid activation and verification solutions. Our products safeguard media and AI investments, while maximizing performance and efficacy on those channels.
• Headquartered in Los Angeles with global offices across New York, Chicago, London, Toronto, Singapore, and more, Zefr is redefining what trust and transparency means for social media in the age of AI.

What you'll do:

• We're hiring a Software Development Engineer in Test (SDET) with 1–3 years of experience to help own quality across our APIs, web apps, data pipelines and AI-powered services, mainly by writing code rather than running manual test scripts.
• You'll work inside product engineering teams. You'll decide what to test and how, build and maintain automated tests in a shared framework, and make sure those tests gate every release. You'll also watch production through monitors and alerts.
• This role suits an engineer who treats testing as a software problem and enjoys finding where systems break. That includes data and model outputs, not only screens and endpoints.
• Plan testing for each feature.
• Join grooming, ask clarifying questions, and write test plans that cover happy paths, edge cases and negative cases.
• Build and maintain test automation for REST and GraphQL APIs, backend services, web UIs and third-party partner APIs, in a shared code-based framework. Review teammates' test code.
• Validate data pipelines and data quality.
• Write checks on pipeline outputs (schemas, row counts, freshness, business rules) and help define data SLAs.
• Test AI and ML features.
• Compare model or prompt versions on fixed datasets, validate shadow deployments before rollout, and test AI agents and their tools (such as MCP servers) for correct behavior, input validation and error handling.
• Keep tests running in CI/CD.
• Wire suites into pipelines so they gate merges and deploys. Keep them fast and reliable by triaging failures, fixing flaky tests and cutting run time.
• Validate releases in preview, staging and production: prepare test data, run regression, sign off, and verify after deploy.
• Monitor production quality.
• Build synthetic checks, dashboards and alerts that catch regressions and bad data before customers do.
• Investigate issues and incidents.
• Reproduce problems, find root causes, write clear bug reports and verify fixes. Help out during high-severity incidents.
• Work across roles with engineers, product managers, data scientists and customer-facing teams. Turn findings into well-scoped tickets and share quality status openly.

What you bring:

• 1–3 years of experience, ideally as a QA engineer or SDET, with hands-on test automation.
• Solid coding skills in at least one language used for test automation. Python is preferred; Java, TypeScript or Go also work.
• Experience automating API tests: request and response validation, schema checks and negative cases.
• Experience with a modern UI end-to-end framework such as Playwright, Cypress or Selenium.
• Working SQL. You can query across tables to confirm data is complete and correct.
• Good test design instincts: edge cases, boundary values, negative cases and the test pyramid.
• Comfort with Git, pull requests, code review and running tests in CI/CD pipelines.
• Persistence when debugging: reading logs, inspecting network calls and querying databases.
• Clear written communication: bug reports, test plans and short async status updates.
• Nice to have: None of these are required. If you have some of them, tell us.
• Exposure to a public cloud (GCP, AWS or Azure) and containers.
• Testing data pipelines and orchestration tools (Airflow, dbt, Great Expectations, Elementary) or data warehouses (Snowflake, BigQuery).
• Testing ML models or LLM-powered features, including evaluation datasets and handling non-deterministic outputs.
• Testing AI agents or MCP servers, or using AI coding assistants to speed up test writing and exploratory testing.
• Observability and alerting tools (for example Datadog, Grafana, Cloud Monitoring, PagerDuty) and synthetic monitoring.
• Performance and load testing (k6, Locust, JMeter).
• Experience with ad tech, digital media or large third-party platform APIs (Google Ads, Meta, TikTok, Snap).
• Frontend knowledge (React) to test framework upgrades and migrations.
• Security basics for test environments, such as managing secrets for test accounts.