Senior Software Development Engineer In Test

About Eightfold

Eightfold is a global leader in AI-native enterprise talent platforms, trusted by many of the world's largest Fortune 500 organizations. Our platform is built from the ground up to operate at scale across Azure and AWS, deployed in multiple regions globally, including IL4-compliant environments for the U.S. Government, supporting users in 100 countries and 30 languages. Eightfold is at the forefront of agentic AI, delivering intelligent agents that actively drive outcomes across hiring and talent workflows, while much of the industry is still in the experimental phase.

Backed by $410M in funding and valued at $2B , we are defining the next era of agentic talent systems. What sets Eightfold apart is not just our technology and mission, but our people. We are a deeply technical, execution-driven organization that values ownership, collaboration, and high standards. Our teams work closely across functions and locations to build systems that scale in real-world enterprise environments.

Eightfold is building the AI platform that powers talent decisions for enterprises worldwide. As our Test Architect, you own the overall quality of what we ship --- across a complex, multi-tenant SaaS platform and the AI models and copilots at its core. This is a hands-on architecture role: you'll set the quality strategy, build the frameworks and guardrails that teams test against, and pioneer how we assure AI systems --- from deep-learning matching models to generative-AI agents --- at production scale.

You won't just catch defects; you'll design the system that makes quality inevitable.

• Define and drive the end-to-end test architecture, standards, and quality strategy across all products and services.
• Own the test strategy across layers --- unit, API/contract, integration, UI, end-to-end --- and the frameworks teams build on.
• Design quality gates into CI/CD so quality shifts left; make "green build = releasable" true and trustworthy.
• Lead non-functional testing: performance and scalability, reliability/resilience, security, and data quality across a high-volume, multi-tenant system.
• Build test data management and environment strategy for a platform handling sensitive candidate and employee data.
• Define the quality metrics that matter (coverage, escape rate, flakiness, MTTR, release confidence) and hold the org to them.
• Mentor and raise the bar for SDETs and QA engineers; influence engineering-wide quality culture without needing authority.

• Own quality for LLM-powered copilots and agents --- groundedness/hallucination checks, prompt and output regression, guardrail and safety testing, and red-teaming.
• Drive AI-accelerated testing : AI-generated test cases, self-healing automation, intelligent test triage and coverage analysis, and synthetic test-data generation --- using AI to make the whole QA function faster and smarter.

• Bachelors Degree in Engineering (B.E/B.Tech in computer science or related discipline is preferred)
• 3 years in software quality/engineering ,owning quality at platform scale.
• Deep automation expertise --- strong coding ability in one or more of Python, Java, or JavaScript/Type Script, and frameworks like Playwright, Selenium, Cypress, or similar; solid API/contract testing.
• Proven design of test architecture and CI/CD quality gates (e.g., Jenkins, Git Hub Actions) in cloud-native environments (AWS/GCP/Azure, Docker, Kubernetes).
• Strength in non-functional testing --- performance/load (k6, JMeter, Gatling, or similar), reliability, and security fundamentals.
• Experience testing data- or ML-driven systems, or a clear track record of ramping fast into new technical domains and a genuine pull toward AI quality.
• The judgment and influence to set standards across many teams, and the communication to make quality a shared goal, not a gate.
• Strong problem solving and analytical skills
• Excellent verbal and written communication skills
• Ability to work independently and within a team structure

• Hands-on experience building LLM/GenAI evaluations (RAG evaluation, prompt regression, LLM-as-judge, eval frameworks) or testing ML pipelines and model performance.
• Familiarity with responsible-AI concepts: bias/fairness metrics, model explainability, AI governance.
• Enterprise multi-tenant SaaS background; HR-tech or other regulated/high-compliance domains a plus.
• Experience introducing AI-assisted tooling into a QA or engineering org.