Senior AI Testing Engineer - Voice Tech & Data Platforms

Deepgram; United States; Remote; USD 120,000 - 190,000; Full time

Job summary

Deepgram is seeking a Software Test Engineer to design, build, and maintain automated test frameworks across products, models, APIs, and data platforms. You will translate product requirements into test strategies and collaborate with QA, Research, Product, Data, and Engineering.

You’ll create regression tests, data-quality gates, load tests, and release criteria, ensuring scalable automation and reliable customer workflows in a fast-paced AI environment.

Qualifications

• 5+ years of professional software or QA engineering experience.
• Strong backend/scripting experience in Python, Rust, Go.
• Experience designing and building automated test pipelines, evaluation frameworks, or data-processing systems.
• Strong analytical skills and comfort reasoning about metrics, thresholds, and statistical variation in results.
• Ability to take charge of ambiguous technical challenges and communicate effectively across teams.

Responsibilities

• Define and execute test plans across Deepgram's products, APIs, SDKs, model-powered features, and data platforms, ensuring production software is robust, reliable, and performs well.
• Design, build, and maintain automated test suites and frameworks for functional, integration, end-to-end, regression, API, browser, and service-level testing across batch and streaming workflows.
• Translate product requirements and customer acceptance criteria into clear test strategies, repeatable test cases, and enforceable release gates.
• Build and maintain representative, customer-focused, and adversarial test datasets, fixtures, and test environments that exercise real-world inputs, edge cases, failure modes, and system limits.
• Validate model-powered behavior—including speech-to-text, text-to-speech, and other AI features—using appropriate metrics, expected outputs, human review, and regression coverage.
• Build testing infrastructure, including test harnesses, reusable scripts, test-data tooling, result-aggregation pipelines, dashboards, and visualizations that make quality signals easy to understand and act on.
• Integrate automated tests, quality checks, canaries, and release validation into CI/CD so regressions are detected continuously rather than through manual testing alone.
• Partner with Engineering, Product, Research, Data, Infrastructure, and DevOps to understand system behavior, dependencies, variations, performance limits, and deployment risks, and to establish appropriate test coverage.
• Test data ingestion, processing, annotation, and quality-control workflows, validating data integrity, completeness, representativeness, deduplication, leakage, and downstream readiness.
• Execute staging and production validation, load and reliability testing, cross-browser and customer-workflow testing, and user acceptance testing in partnership with internal stakeholders and customer QA teams.
• Maintain and improve the test-case repository, automation coverage, test documentation, and release-readiness reporting so teams have a clear view of what was tested, what passed, and what remains risky.
• Write precise, actionable bug reports with reproducible steps, inputs, parameters, expected and actual results, logs or artifacts, and clear severity; participate in triage and elevate issues when necessary.
• Help raise the bar through code reviews, test-design reviews, technical discussions, and strong engineering, automation, and QA practices.

Skills

Strong analytical skills

Effective communication

Ambiguity handling

Education

BS, MS, or PhD in Computer Science, AI, Applied Math, or related field

Tools

Python

Rust

Go

Job description

Deepgram is seeking a Software Test Engineer to design, build, and maintain automated test frameworks across products, models, APIs, and data platforms. You will translate product requirements into test strategies and collaborate with QA, Research, Product, Data, and Engineering.

You’ll create regression tests, data-quality gates, load tests, and release criteria, ensuring scalable automation and reliable customer workflows in a fast-paced AI environment.